Biophysica

Innate immunity is the body’s first line of defense for mounting robust antiviral signaling. However, the role of cytoskeletal prestress, a hallmark of cellular mechanotransduction, in regulating innate immune pathways such as retinoic acid-inducible gene I (RIG-I) signaling remains poorly understood. Herein, we show that cells on soft vs. rigid substrates elicit cytoskeletal prestress-dependent activation of RIG-I signaling, leading to differential type-I interferon (IFN) gene expression. Cells were cultured on soft (0.6 kPa) and stiff (8.5 kPa) substrates to modulate cellular traction and prestress, followed by transfection of Poly(I:C), a synthetic viral dsRNA mimic, to measure the RIG-I-mediated innate immune response. Cells on soft substrates show minimal activation of RIG-I signaling, resulting in low expression of IFN-β1 and other IFN-stimulated genes (ISGs), compared to cells on stiff substrates. We further demonstrate that activation of TANK Binding Kinase 1 (TBK1), a downstream effector of the RIG-I pathway, is inhibited in cells on soft substrates due to the cytoplasmic sequestration of the Yes-associated protein (YAP), a HIPPO pathway effector protein. In contrast, cells on stiffer substrates experienced decreased TBK1 inhibition due to the nuclear localization of YAP and exhibited elevated TBK1 activation and heightened IFN and ISG expressions. Together, we demonstrate that cytoskeletal prestress represents a key biophysical regulator of innate immune signaling. Full article

Large-scale computer simulations were employed to investigate the conformational response of the spike protein components S1 and S2 using a coarse-grained model. Temperature was systematically varied to assess the balance between stabilizing residue–residue interactions and thermal fluctuations. The resulting contact profiles reveal distinct segmental reorganization and self-assembly behaviors between S1 and S2. At lower, thermoresponsive temperatures, pronounced segmental globularization occurs in the N-terminal domain (NTD; M153–K202) and receptor-binding domain (RBD; E406–E471) of S1, whereas S2 exhibits alternating regions of high and low contact density. Increasing temperature reduces this segmental globularization, leaving only minor persistence at elevated temperatures. The temperature dependence of the radius of gyration (R_g) further demonstrates the contrasting thermal behaviors of S1 and S2. For S1, R_g increases continuously and monotonically with temperature, reaching a steady-state value approximately 50% higher than that at low temperature. In contrast, S2 displays a non-monotonic response: R_g initially rises to a maximum nearly sevenfold higher than its low-temperature value, then decreases with further temperature increase. Scaling analysis of the structure factor reveals that the globularity of S1 diminishes significantly upon heating, while S2 becomes modestly more compact yet retains its predominantly fibrous character. Full article

Microencapsulation is a fundamental technology for protecting active compounds from environmental degradation by factors such as light, heat, and oxygen. This process significantly improves their stability, bioavailability, and shelf life by entrapping an active core within a protective matrix. Therefore, a thorough understanding of the physicochemical interactions between these components is essential for developing stable and efficient delivery systems. The composition of the microcapsule and the encapsulation method are key determinants of system stability and the retention of encapsulated materials. Recently, the application of computational tools to predict and optimize microencapsulation processes has emerged as a promising area of research. In this context, molecular dynamics (MD) simulation has become an indispensable computational technique. By solving Newton’s equations of motion, MD simulations enable a detailed study of the dynamic behavior of atoms and molecules in a simulated environment. For example, MD-based analyses have quantitatively demonstrated that optimizing polymer–core interaction energies can enhance encapsulation efficiency by over 20% and improve the thermal stability of active compounds. This approach provides invaluable insights into the molecular interactions between the core material and the matrix, ultimately facilitating the rational design of optimized microstructures for diverse applications, including pharmaceuticals, thereby opening new avenues for innovation in the field. Ultimately, the integration of computational modeling into microencapsulation research not only represents a methodological advancement but also pivotal opportunity to accelerate innovation, optimize processes, and develop more effective and sustainable therapeutic systems. Full article

This study explored the use of physical methods, namely X-ray diffraction, atomic force microscopy, and energy-dispersive X-ray spectroscopy, to analyze the structure and composition of tear fluid desiccates. Tear samples were collected from patients with dry eye syndrome, glaucoma, and multiple sclerosis. Our results revealed significant differences in the crystallization patterns, chemical composition, and morphology of tear fluid among the disease groups compared to healthy individuals. XRD analysis identified variations in salt crystallization within tear fluid desiccates. AFM provided nanoscale morphological visualization. EDX determined the presence of key chemical elements. Our findings showed that changes in crystallization and unbalance of ionic composition in tear fluid may serve as potential markers for diagnosing ocular diseases. This study highlights the potential of these techniques for non-invasive diagnostics and contributes to the development of innovative strategies for monitoring structural properties in tear fluid desiccates of analyzed inflammatory, and neurodegenerative diseases. Full article

Human granulocyte colony-stimulating factor (hG-CSF) is primarily used to treat neutropenia induced by cancer chemotherapy and bone marrow transplantation. The current identification test for hG-CSF relies on Western blot (WB), a labor-intensive and technically demanding method. This study aimed to screen and prepare an anti-hG-CSF nanobody to identify and quantify hG-CSF, with the ultimate goal of developing colloidal gold-labeled nanobody test strips for rapid identification. An alpaca was immunized with hG-CSF, and the VHH gene sequence encoding the anti-hG-CSF nanobody was obtained through sequencing following phage display library construction and multiple rounds of biopanning. The nanobody C68, obtained from screening, was expressed by E. coli, and its physicochemical properties such as molecular weight, isoelectric point, and affinity were characterized after purification. WB analysis demonstrated excellent performance of the nanobody in identification tests in terms of specificity, limit of detection (LOD), applicability with products from various manufacturers, and thermal stability. Additionally, we established an ELISA method for hG-CSF quantification utilizing the nanobody C68 and conducted methodological validation. Finally, colloidal gold-based test strips were constructed using the nanobody C68, with a LOD of 30 μg/mL, achieving rapid identification for hG-CSF. This study represents a novel application of nanobodies in pharmaceutical testing and offers valuable insights for developing identification tests for other recombinant protein drugs. Full article

Post-COVID-19 syndrome (PCS) is a multifactorial disorder comprising different subgroups. Our study aimed to investigate the longitudinal changes in retinal microcirculation in PCS patients. Eighty PCS patients were recruited at the Department of Ophthalmology at the Friedrich-Alexander University of Erlangen-Nürnberg. Retinal microcirculation was measured twice using optical coherence tomography angiography (OCT-A) within the superficial vascular plexus (SVP), intermediate capillary plexus (ICP), deep capillary plexus (DCP), and peripapillary region. Vessel density (VD) was calculated using the Erlangen Angio Tool with an APSified and Bruch’s membrane opening-based analyses. The least-squares means (LS-Means) of VD were 30.4 (SE = 0.168) vs. 30.3 (SE = 0.166) (SVP), 22.4 (SE = 0.143) vs. 22.2 (SE = 0.141) (ICP), 23.9 (SE = 0.186) vs. 23.8 (SE = 0.185) (DCP), and 27.4 (SE = 0.226) vs. 27.0 (SE = 0.224) (peripapillary) in patients with PCS at visits 1 and 2, respectively. The study cohort showed physically stable PCS symptoms with PEM/fatigue and concentration disorders as major symptoms and only a slight, clinically irrelevant improvement of the Bell Score. The multivariate longitudinal model confirmed the clinical observations by showing that VD did not change significantly during follow-up (p = 0.46). Strong interdependencies between the macular layers (p < 0.001) were observed. The data of the present study suggests that while overall APSified macular VD and BMO-based APSified peripapillary VD were stable within a PCS cohort of physically stable PCS symptoms, individual patients may experience coordinated microvascular changes, particularly within the macular plexuses. Together, the results support a model of heterogeneous yet biologically consistent microvascular response in PCS pathophysiology. Full article

A systematic review was conducted to assess the effects of microgravity and space radiation on astronauts’ oral health. This review aimed to determine if these conditions increase the risk of dental and periodontal diseases, identify pre-mission dental care strategies, and specify relevant dental emergencies for astronauts to manage during missions. Following PRISMA guidelines, the review was registered on PROSPERO (CRD42023472765). Databases including PubMed, Scopus, Web of Science, Cochrane Library, and OVID Medline were searched. Of the 13 studies identified, 7 were eligible for qualitative synthesis. The included studies revealed that space conditions compromise oral health. Findings indicate changes in saliva composition, with a significant decline in salivary lysozyme levels during missions lasting 28 to 84 days. Salivary IgA levels also increased before and peaked after flights (microgravity alters fluid shear and protein folding). Viral reactivation was a key finding, with latent viruses such as Epstein–Barr virus (EBV), cytomegalovirus (CMV), and varicella zoster virus (VZV) being reactivated during missions (immune suppression and gene expression shifts under spaceflight stress). Data from a study found that 50% of crew members shed viruses in their saliva or urine, and 38% tested positive for herpesviruses. The included studies also documented alterations in the oral microbiome, including increased gastrointestinal and decreased nasal microbial diversity. This suggests alterations in salivary biomarkers, viral shedding, and microbiome changes in astronauts during long-duration missions. These changes appear associated with immune dysregulation and stress, but causality remains uncertain due to observational designs, small heterogeneous samples, and confounding factors. Although current evidence is indicative rather than definitive, these findings highlight the need for preventive dental measures prior to missions and preparedness for managing oral emergencies in-flight. Future studies should address the mechanistic separation of microgravity and radiation effects, with implications for upcoming Moon and Mars missions. Full article

Myelin is a membranous structure critically important for human health. Historically, it was believed that myelin remained largely unchanged in the adult brain. However, recent research has shown that myelin is remarkably dynamic, capable of adjusting axonal conduction velocity and playing a role in learning, memory, and recovery from injury, in response to both physiological and pathological signals. Axons are more efficiently insulated in myelinated fibers, where segments of the axonal membrane are wrapped by the myelin sheath. Although extensive data are available on the electrical properties of myelin, its structural and mechanical characteristics—as well as the role of its lipid composition—are also relevant, although much less explored. The objective of our review is derived from this point since alterations in lipid components can lead to axonal dysfunction, giving rise to neurological disorders such as multiple sclerosis and other demyelinating conditions. In this review, concerning the lipid composition of myelin, we focus on two distinct classes of lipids: sphingolipids and long-chain fatty acids, emphasizing the differential contributions of saturated versus polyunsaturated species. We analyze studies that correlate the mechanical vulnerability of myelin with its lipid composition, particularly sphingomyelin, thereby underscoring its role in protecting neurons against physical stress and providing a robust microstructural network that reinforces the white matter as a whole. From a biochemical perspective, we examine the susceptibility of myelin to oxidative stress, metabolic disorders, and extreme nutritional deficiencies in relation to the role of long-chain fatty acids. Both perspectives highlight that the aforementioned lipids participate in a complex biomechanical balance that is essential for maintaining the stability of myelin and, consequently, the integrity of the central and peripheral nervous systems. Full article

Humanized mouse models offer human-specific platforms for investigating complex host–pathogen interactions, addressing shortcomings of conventional preclinical models that often fail to replicate human immune responses accurately. This integrative review examines the intersection of advanced morphological phenotyping and artificial intelligence (AI) to enhance predictive capacity and translational relevance in infectious disease research. A structured literature search was conducted across PubMed, Scopus, and Web of Science (2010–2025), applying defined inclusion and exclusion criteria. Evidence synthesis highlights imaging modalities, AI-driven phenotyping, and standardization strategies, supported by comparative analyses and quality considerations. Persistent challenges include variability in engraftment, lack of harmonized scoring systems, and ethical governance. We propose recommendations for standardized protocols, risk-of-bias mitigation, and collaborative training frameworks to accelerate adoption of these technologies in translational medicine. Full article

Metabolic imaging is undergoing a fundamental transformation. Traditionally confined to static representations of metabolite distribution through modalities such as PET, MRS, and MSOT, imaging has offered only partial glimpses into the dynamic and systemic nature of metabolism. This Perspective envisions a shift toward dynamic metabolic intelligence—an integrated framework where real-time imaging is fused with physics-informed models, artificial intelligence, and wearable data to create adaptive, predictive representations of metabolic function. We explore how novel technologies like hyperpolarized MRI and time-resolved optoacoustics can serve as dynamic inputs into digital twin systems, enabling closed-loop feedback that not only visualizes but actively guides clinical decisions. From early detection of metabolic drift to in silico therapy simulation, we highlight translational pathways across oncology, cardiology, neurology, and space medicine. Finally, we call for a cross-disciplinary effort to standardize, validate, and ethically implement these systems, marking the emergence of a new paradigm: metabolism as a navigable, model-informed space of precision medicine. Full article

Heme enzymes that bind and reduce O₂ are susceptible to poisoning by NO. The high reactivity and affinity of NO for ferrous heme produces stable ferrous-NO complexes, which in theory should preclude O₂ binding and turnover. However, NO inhibition is often competitive with respect to O₂ and rapidly reversible, thus providing cellular and organismal survival advantages. This kinetic paradox has prompted a search for mechanisms for reversal and hence resistance. Here, I critically review proposed resistance mechanisms for NO dioxygenase (NOD) and cytochrome c oxidase (CcO), which substantiate reduction or oxidation of the tightly bound NO but nevertheless fail to provide kinetically viable solutions. A ferrous heme intermediate is clearly not available during rapid steady-state turnover. Reversible inhibition can be attributed to NO competing with O₂ in transient low-affinity interactions with either the ferric heme in NOD or the ferric heme-cupric center in CcO. Toward resolution, I review the underlying principles and evidence for kinetic control of ferric heme reduction via an O₂-triggered ferric heme spin crossover and an electronically-forced motion of the heme and structurally-linked protein side chains that elicit electron transfer and activate O₂ in the flavohemoglobin-type NOD. For CcO, kinetics, structures, and density functional theory point to the existence of an analogous O₂ and reduced oxygen intermediate-controlled electron-transfer gate with a linked proton pump function. A catalytic cycle and mechanism for CcO is finally at hand that links each of the four O₂-reducing electrons to each of the four pumped protons in time and space. A novel proton-conducting tunnel and channel, electron path, and pump mechanism, most notably first hypothesized by Mårten Wikström in 1977 and pursued since, are laid out for further scrutiny. In both models, low-energy spin-orbit couplings or ‘spintronic’ interactions with O₂ and NO or copper trigger the electronic motions within heme that activate electron transfer to O₂, and the exergonic reactions of transient reactive oxygen intermediates ultimately drive all enzyme, electron, and proton motions. Full article

Medical physicists are transforming physiological measurements and electrophysiological applications by addressing challenges like motion artifacts and regulatory compliance through advanced signal processing, artificial intelligence (AI), and statistical rigor. Their innovations in wearable electrophysiology achieve 8–12 dB signal-to-noise ratio (SNR) improvements in EEG, 60% motion artifact reduction, and 94.2% accurate AI-driven arrhythmia detection at 12 μW power. In precision neurology, machine learning (ML) with evoked potentials (EPs) predicts spinal cord injury (SCI) recovery and multiple sclerosis (MS) progression with 79.2% accuracy based on retrospective data from 560 SCI/MS patients. By integrating multimodal data (EPs, MRI), developing quantum sensors, and employing federated learning, these can enhance diagnostic precision and prognostic accuracy. Clinical applications span epilepsy, stroke, cardiac monitoring, and chronic pain management, reducing diagnostic errors by 28% and optimizing treatments like deep brain stimulation (DBS). In this paper, we review the current state of wearable devices and provide some insight into possible future directions. Embedding medical physicists into standardization efforts is critical to overcoming barriers like quantum sensor power consumption, advancing personalized, evidence-based healthcare. Full article

Cryo-electron microscopy single-particle analysis (cryo-EM SPA) has advanced three-dimensional protein structure determination, yet resolving intrinsically disordered proteins and regions (IDPs/IDRs) remains challenging due to conformational heterogeneity. This research evaluates cryo-EM’s capacity to map dynamic regions, assesses the adaptability of disorder prediction tools, and explores optimization strategies for dynamic structure prediction. Cryo-EM SPA datasets from 2000 to 2024 were categorized into different periods, forming a database integrating sequence data and disorder indices. Established prediction tools—AlphaFold2 (pLDDT), flDPnn, and IUPred—were evaluated for transferability, while a multi-level CLTC hybrid model (combining CNN, LSTM, Transformer, and CRF architectures) was developed to link local conformational fluctuations with global sequence contexts. Analyses revealed consistent advancements in average resolution and model counts over the past decade, although mapping disordered regions remained technically demanding. Both the adapted AlphaFold pLDDT and the CLTC model demonstrated efficacy in predicting structurally variable and poorly resolved regions. A subset of the cryo-EM missing residues exhibited intermediate conformational features, suggesting classification ambiguities potentially influenced by experimental conditions. These findings systematically outline the evolving capabilities of cryo-EM in resolving dynamic regions, benchmark the adaptability of computational tools, and introduce a hybrid model to enhance prediction accuracy. This study provides a framework for addressing conformational heterogeneity, contributing to methodological advancements in structural biology. Full article

Organs-on-Chips (OoC) technology has begun to be considered a pragmatic tool for drug evaluation, offering researchers an opportunity to move beyond the less physiologically relevant animal models. OoCs are microfluidic structures that imitate the functionalities of individual human organs, serving as mimicry tools for drug response and reproducibility studies. On the one hand, companies producing OoCs find managing and analyzing the large amounts of data generated challenging. This is where artificial intelligence (AI) can be deployed to address such problems. This paper will present the state-of-the-art of current OoC technology and AI, discussing the benefits and threats of combining these approaches. AI can be applied to optimize the process of OoC fabrication and operation, as well as for the big data analysis of OoC devices. By combining these technologies, scientists gain a powerful tool for drug development that is more efficient and accurate. However, processing the vast datasets generated by OoC systems often requires specialized AI expertise and computational resources. Despite the numerous possible benefits of amalgamating OoC technology with AI, several challenges and limitations need to be addressed. The large datasets generated by OoC systems can be difficult to process and analyze, which is a task that may require specialized AI expertise. Additionally, limitations of OoC systems include issues with reproducibility, as the devices are sensitive to perturbations in experimental conditions. Furthermore, the development and implementation of AI algorithms require significant computational resources and expertise, which may not be readily available to all research institutions. To overcome these challenges, interdisciplinary collaboration between biologists, engineers, data scientists, and AI experts is essential. Continued advancements in both OoC technology and AI will likely lead to more robust and versatile platforms for biomedical research and drug development, ultimately contributing to the advancement of personalized medicine and the reduction of reliance on animal testing. Full article

Type 2 diabetes (T2D) is a chronic metabolic disorder requiring effective management to avoid complications. Metformin is a first-line drug agent and is routinely prescribed for the control of glycemia, but its underlying dynamics are complicated and not fully quantified. This paper formulates a control-oriented and interpretable mathematical model that integrates metformin dynamics into a classic beta-cell–insulin–glucose (BIG) regulation system. The paper’s applicability to theoretical and clinical settings is enhanced by rigorous mathematical analysis, which guarantees the model is globally bounded, well-posed, and biologically meaningful. One of the key features of the study is its global stability analysis using Lyapunov functions, which demonstrates the asymptotic stability of critical equilibrium points under realistic physiological constraints. These findings support the predictive reliability of the model in explaining long-term glycemic regulation. Bifurcation analysis also clarifies the dynamic interplay between glucose production and utilization by identifying parameter thresholds that signify transitions between homeostasis and pathological states. Residual analysis, which detects Gaussian-distributed errors, underlines the robustness of the fitting process and suggests possible refinements by including temporal effects. Sensitivity analysis highlights the predominant effect of the initial dose of metformin on long-term glucose regulation and provides practical guidance for optimizing individual treatment. Furthermore, changing the two considered metformin parameters from their optimal values—altering the dose by ±50% and the decay rate by ±20%—demonstrates the flexibility of the model in simulating glycemic responses, confirming its adaptability and its potential for optimizing personalized treatment strategies. Full article

The overexpression of atypical protein kinase C-iota (PKC-ι) is a biomarker for carcinogenesis in various cell types, such as glioma, ovarian, renal, etc., manifesting as a potential drug target. In previous in vitro studies, ICA-1S and ICA-1T, experimental candidates for inhibiting PKC-ι, have demonstrated their specificity and promising efficacy against various cancers. Moreover, the in vivo studies have demonstrated low toxicity levels in acute and chronic murine models. Despite these prior developments, the binding affinities of the inhibitors were never thoroughly explored from a biophysical perspective. Here, we present the biophysical characterizations of PKC-ι in combination with ICA-1S/1T. Various methods based on molecular docking, light scattering, intrinsic fluorescence, thermal denaturation, and heat exchange were applied. The biophysical characteristics including particle sizing, thermal unfolding, aggregation profiles, enthalpy, entropy, free energy changes, and binding affinity (K_d) of the PKC-ι in the presence of ICA-1S were observed. The studies indicate the presence of domain-specific stabilities in the protein–ligand complex. Moreover, the results indicate a spontaneous reaction with an entropic gain, resulting in a possible entropy-driven hydrophobic interaction and hydrogen bonds in the binding pocket. Altogether, these biophysical studies reveal important insights into the binding interactions of PKC-ι and its inhibitors ICA-1S/1T. Full article

Protein purification is often performed for various applications. However, enzyme purification processes typically involve multiple steps that reduce yield and increase production costs. To overcome these challenges, we developed a novel three-step process to purify a lipase from whole sardine viscera (WSV), leveraging protein properties and the structural affinity of lipases for dye ligands. A crude extract of the viscera (CEV) was obtained by grinding the whole viscera in 50 mM phosphate buffer (pH 7.0, Solution B) followed by centrifugation (6000× g; 30 min, 0 °C). Lipolytic activity (3.3 U/mg) was recorded only in the supernatant. The purification process began with ammonium sulfate fractionation (30–50% saturation), increasing lipolytic activity in the precipitate (PF30-50) to 32.9 U/mg. PF30-50 was then ultrafiltered using a 30 KDa MWCO membrane, where 5% of semi-purified lipases (SPLSV) was retained with an activity of 156.5 U/mg (UF30). Finally, the SPLSV was injected into a column packed with dye ligand affinity adsorbent, pre-equilibrated with 1.0 M ammonium sulfate in buffer A. The WSV lipase was eluted using a step gradient to progressively reduce salt concentration. SDS-PAGE analysis revealed a single band of purified lipase from sardine viscera (PLSV) corresponding to a molecular weight of 123.4 kDa, with a specific activity of 266.4 U/mg. The combination of ammonium sulfate precipitation, ultrafiltration, and dye-ligand affinity chromatography provides a scalable and reproducible approach with potential industrial relevance, particularly in biocatalysis and waste valorization contexts. Full article

Protein polarimetry has been evaluated as a simple and straightforward technique to detect the cryptic denaturation of exemplary proteins. The general rules of rotation vs. amino acid and structural composition and the respective knowledge gaps were reviewed, and the specific rotation of cystine was determined in 4 M NaCl solution as [α]_D²⁰ = –302.5°. The specific rotations at 589 nm and 436 nm and the ratio were measured for several model proteins, some purified plasma-derived proteins and for three monoclonal antibodies. The immunoglobulin G concentrates all showed a narrow ratio range likely characteristic for this protein class. Heat denaturation experiments were conducted at temperatures between 50 and 85 °C both for short-time (10 min) and for prolonged periods of heat exposure (up to 210 min). Denaturation by heat resulted not only in the known levorotatory shift, but also in a shift in the specific rotation ratio. The stabilizing effect of fatty acids in bovine serum could be demonstrated by this parameter. Polarimetry thus appears to be a particularly sensitive and simple method for the characterization of the identity and the thermal stability of proteins and should therefore be added again as a complimentary method to the toolbox of protein chemistry. Full article

Protein enzymes are highly efficient catalysts that exhibit adaptability and selectivity under diverse biological conditions. In some organisms, such as bacteria, structurally similar enzymes, for instance, shikimate kinase (SK) and adenylate kinase (AK), coexist and act on chemically related ligands. This raises the question of whether these enzymes can accommodate and potentially react with each other’s ligands. In this study, we investigate the stability of non-cognate ligand binding in SK and explore whether external electric fields (EFs) can modulate this interaction, leading to cross-reactivity in SK. Using molecular dynamics simulations, we assess the structural integrity of SK and the binding behavior of ATP and AMP under EF-off and EF-on cases. Our results show that EFs enhance protein structure stability, stabilize non-cognate ligands in the binding pocket, and reduce local energetic frustration near the R116 residue located in the binding site. In addition to this, dimensionality reduction analyses reveal that EFs induce more coherent protein motions and reduce the number of metastable states. Together, these findings suggest that external EFs can reshape enzyme–ligand interactions and may serve as a tool to modulate enzymatic specificity and functional promiscuity. Thus, we provide computational evidence that supports the concept of using an EF as a tunable parameter in enzyme engineering and synthetic biology. However, further experimental investigation would be valuable to assess the reliability of our computational predictions. Full article