Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Higher prevalence of thyroid-specific autoantibodies (TPOAb and TgAb) is related to a higher prevalence of fractures in females: results from NHANES 2007-2010.

A retrospective analysis was conducted using data from the NHANES. Bone mineral density (BMD) was compared in different thyroid-specific autoantibodies groups. Strengths of associations were calculated by using binary logistic regression models. Higher titers of thyroid-specific autoantibodies (TgAb and/or TPOAb) may lead to decreased BMD. Higher prevalence of TgAb and TPOAb significantly associated with fractures in females but not in males. PURPOSE: Hashimoto's thyroiditis is characterized by elevated thyroid-specific autoantibodies. It is currently believed that osteoporosis is not only a disease with abnormal mineral metabolism but also with immune abnormalities. This study investigated the relationship between thyroid-specific autoantibodies and osteoporosis, including the bone mineral density (BMD) values and fractures. METHODS: A retrospective analysis was conducted using data from the National Health and Nutrition Examination Survey (2007-2010). BMD was compared in different thyroid-specific autoantibodies groups. The associations between thyroid-specific autoantibodies and fractures were explored. Strengths of associations were calculated by binary logistic regression models. Candidate variables for binary logistic regression model were selected after screened in univariate analysis (variables with P < 0.05). RESULTS: A total of 3865 study participants were included in this analysis; 224 participants were TgAb positive and 356 were TPOAb positive. A total of 392 participants reported hip, spine or wrist fractures. Participants with higher prevalence of TgAb or TPOAb had lower BMD. In females, significant cigarettes use, higher prevalence of TgAb and TPOAb, and the BMD of the total femur and femoral neck were significantly associated with fractures. Higher prevalence of TPOAb was particularly associated with a higher possibility of hip or spine fractures. In males, significant cigarettes use, 25OHD3, the BMD values of the total femur, femoral neck and total spine were significantly associated with fractures. CONCLUSION: Higher prevalence of thyroid-specific autoantibodies may lead to decreased BMD. In females, higher prevalence of TgAb and TPOAb significantly associated with fractures and TPOAb especially relating to the fractures of hip and spine. Males patients with vitamin D deficiency or insufficiency associated a higher possibility of fractures.

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

How does Hashimoto's thyroiditis affect bone metabolism?

Bone marrow contains resident cellular components that are not only involved in bone maintenance but also regulate hematopoiesis and immune responses. The immune system and bone interact with each other, coined osteoimmunology. Hashimoto's thyroiditis (HT) is one of the most common chronic autoimmune diseases which is accompanied by lymphocytic infiltration. It shows elevating thyroid autoantibody levels at an early stage and progresses to thyroid dysfunction ultimately. Different effects exert on bone metabolism during different phases of HT. In this review, we summarized the mechanisms of the long-term effects of HT on bone and the relationship between thyroid autoimmunity and osteoimmunology. For patients with HT, the bone is affected not only by thyroid function and the value of TSH, but also by the setting of the autoimmune background. The autoimmune background implies a breakdown of the mechanisms that control self-reactive system, featuring abnormal immune activation and presence of autoantibodies. The etiology of thyroid autoimmunity and osteoimmunology is complex and involves a number of immune cells, cytokines and chemokines, which regulate the pathogenesis of HT and osteoporosis at the same time, and have potential to affect each other. In addition, vitamin D works as a potent immunomodulator to influence both thyroid immunity and osteoimmunology. We conclude that HT affects bone metabolism at least through endocrine and immune pathways.

Mitochondrial iron overload mediated by cooperative transfer of plasma membrane ATP5B and TFR2 to mitochondria triggers hepatic insulin resistance under PFOS exposure.

In general populations, insulin resistance (IR) is related to perfluorooctane sulfonate (PFOS), a persistent organic pollutant. However, the underlying mechanism remains unclear. In this study, PFOS induced mitochondrial iron accumulation in the liver of mice and human hepatocytes L-O2. In the PFOS-treated L-O2 cells, mitochondrial iron overload preceded the occurrence of IR, and pharmacological inhibition of mitochondrial iron relieved PFOS-caused IR. Both transferrin receptor 2 (TFR2) and ATP synthase ß subunit (ATP5B) were redistributed from the plasma membrane to mitochondria with PFOS treatment. Inhibiting the translocation of TFR2 to mitochondria reversed PFOS-induced mitochondrial iron overload and IR. In the PFOS-treated cells, ATP5B interacted with TFR2. Stabilizing ATP5B on the plasma membrane or knockdown of ATP5B disturbed the translocation of TFR2. PFOS inhibited the activity of plasma-membrane ATP synthase (ectopic ATP synthase, e-ATPS), and activating e-ATPS prevented the translocation of ATP5B and TFR2. Consistently, PFOS induced ATP5B/TFR2 interaction and redistribution of ATP5B and TFR2 to mitochondria in the liver of mice. Thus, our results indicated that mitochondrial iron overload induced by collaborative translocation of ATP5B and TFR2 was an up-stream and initiating event for PFOS-related hepatic IR, providing novel understandings of the biological function of e-ATPS, the regulatory mechanism for mitochondrial iron and the mechanism underlying PFOS toxicity.

Global transfer of salinization on irrigated land: Complex network and endogenous structure.

Increasing globalization intensifies land redistribution via global supply chains. Interregional trade not only transfers embodied land but also displaces the negative environmental impact of land degradation from one region to another. This study sheds light on land degradation transfer by focusing on salinization directly whereas previous studies have extensively assessed the land resource embodied in trade. To analyze the relationships among economies under interwoven embodied flows, this study integrates complex network analysis and input-output method to observe the endogenous structure of the transfer system. By focusing on irrigated land with higher crop yields than dryland farming, we make policy recommendations on food safety and proper irrigation. The results of the quantitative analysis show that the total amount of saline and sodic irrigated land embodied in global final demand are 260978.23 and 424291.05 square kilometers respectively. Salt-affected area of irrigated land is imported by not only developed countries but also large developing countries such as Mainland China and India. Exports of embodied salt-affected land in Pakistan, Afghanistan, and Turkmenistan are pressing issues, accounting for nearly 60% of total exports from net exporters worldwide. It is also demonstrated that embodied transfer network has a basic community structure of three groups due to regional preference in agricultural products trade.

ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction.

Lipophilicity (logD) and aqueous solubility (logSw) play a central role in drug development. The accurate prediction of these properties remains to be solved due to data scarcity. Current methodologies neglect the intrinsic relationships between physicochemical properties and usually ignore the ionization effects. Here, we propose an attention-driven mixture-of-experts (MoE) model named ALipSol, which explicitly reproduces the hierarchy of task relationships. We adopt the principle of divide-and-conquer by breaking down the complex end point (logD or logSw) into simpler ones (acidic pKa, basic pKa, and logP) and allocating a specific expert network for each subproblem. Subsequently, we implement transfer learning to extract knowledge from related tasks, thus alleviating the dilemma of limited data. Additionally, we substitute the gating network with an attention mechanism to better capture the dynamic task relationships on a per-example basis. We adopt local fine-tuning and consensus prediction to further boost model performance. Extensive evaluation experiments verify the success of the ALipSol model, which achieves RMSE improvement of 8.04%, 2.49%, 8.57%, 12.8%, and 8.60% on the Lipop, ESOL, AqSolDB, external logD, and external logS data sets, respectively, compared with Attentive FP and the state-of-the-art in silico tools. In particular, our model yields more significant advantages (Welch's t-test) for small training data, implying its high robustness and generalizability. The interpretability analysis proves that the atom contributions learned by ALipSol are more reasonable compared with the vanilla Attentive FP, and the substitution effects in benzene derivatives agreed well with empirical constants, revealing the potential of our model to extract useful patterns from data and provide guidance for lead optimization.

[Methimazole Tablets-Induced Algospasm: Two Cases Report].

Here, we reported two cases with hyperthyroidism who complained of myalgia and muscle cramps during treatment with methimazole tablets (or Thyrozol, the brand name). One case experienced muscle cramps after taking Thyrozol for 6 months, and by this time the patient's thyroid function had returned to normal. In the other case, pain caused by muscular cramps began after the patient took Thyrozol for two weeks and the patient's thyroid function had not returned to normal yet at the time. In both cases, pain caused by muscle cramps appeared while the patients were taking Thyrozol. The myalgia persisted in spite of a reduction in the Thyrozol dose, but was significantly relieved with the discontinuation of Thyrozol. Myalgia and muscle cramps did not recur after the patients were switched to methimazole ointment. There was a strong temporal association between oral administration of Thyrozol and pain caused by muscle cramps, which may indicate that myalgia and muscle cramps are adverse reactions of Thyrozol. Looking into the relevant literature on the topic, we explored in this report the possible mechanisms of the onset of muscle cramps associated with Thyrozol, and compared the adverse reactions of two different formulations of methimazole, intending to provide more clinical experience for the treatment of hyperthyroidism and the management of rare adverse reactions related to antithyroid drugs.

Color mismatch and observer metamerism between conventional liquid crystal displays and organic light emitting diode displays.

Organic light emitting diode (OLED) displays use red, green, and blue primaries with a higher saturation level to produce larger color gamuts than conventional liquid crystal displays (LCD). No past study, however, experimentally investigated how such a difference between these two display types causes color mismatch and observer metamerism using the most widely used color matching functions (CMFs)-the CIE 1931 2° CMFs-for color calibration and specification. In this study, 50 human observers performed color matching tasks for six color stimuli with a field-of-view of 4.77° between four test displays (i.e., one LCD and three OLED) and a reference OLED display. The color gamuts of the LCD and OLED displays were similar to the sRGB and P3 standard color gamuts. It was found the CIE 1931 2° CMFs cannot accurately characterize the color matches between the LCD and OLED displays, with different chromaticities required to produce matched color appearance. Particularly, when the stimuli had matched color appearance, the chromaticities of the stimuli produced by the LCD display were all shifted towards the -u'+v' direction in the CIE 1976 u'v' chromaticity diagram in comparison to those produced by the OLED display. This suggested that using the CIE 1931 2° CMFs for display calibration would cause the colors shown on OLED displays to have a yellow-green tint if those on LCD displays appear neutral. In addition, a larger degree of observer metamerism was found between the LCD and OLED displays, while little differences, in terms of color mismatch and observer metamerism, were found between the OLED displays. The CIE 2006 2° CMFs were found to have better performance than the CIE 1931 2°, 1964 10°, and 2006 10° CMFs, which could be partially due to the size of the stimulus used in the experiment.

Manipulation of radial-variant polarization for creating tunable bifocusing spots.

We propose and generate a new radial-variant vector field (RV-VF) with a distribution of states of polarization described by the square of the radius and exploit its focusing property. Theoretically, we present the analytical expressions for the three-dimensional electric field of the vector field focused by a thin lens under the nonparaxial and paraxial approximations based on the vectorial Rayleigh-Sommerfeld formulas. Numerical simulations indicate that this focused field exhibits bifocusing spots along the optical axis. The underlying mechanism for generating the bifocusing property is analyzed in detail. We give the analytical formula for the interval between two foci. Experimentally, we generate the RV-VFs with alterable topological charge and demonstrate that the interval between two foci is controllable by tuning the radial topological charge. This particular focal field has specific applications for biparticle trapping, manipulating, alignment, transportation, and accelerating along the optical axis.

Comparison of treatment outcome between glucocorticoids and non-steroidal anti-inflammatory drugs in subacute thyroiditis patients-a systematic review and meta-analysis.

Importance: Subacute thyroiditis (SAT) is a self-limiting and inflammatory thyroid disease. Although SAT usually improves on its own within weeks, it needs treatment when patients have pain, fever, and symptoms of thyrotoxicosis. Therapeutic drugs mainly include non-steroidal anti-inflammatory drugs (NSAIDs) and glucocorticoids. Currently, there is no systematic review or meta-analysis of the comparison of outcomes between NSAIDs and glucocorticoids for the treatment of SAT. Objectives: To conduct a systematic review and meta-analysis on the outcomes in subacute thyroiditis patients treated with glucocorticoids or NSAIDs. Data sources: Using the four electronic databases, including PubMed, Embase, Cochrane Library, Wanfang database and Web of Science. All publications until 21 June 2023 were searched. The reference lists of all selected articles were independently screened to identify additional studies left out in the initial search. Study selection: The literature comparing outcomes between glucocorticoids and non-steroidal anti-inflammatory drugs for patients with subacute thyroiditis will be included. Data extraction and synthesis: Two independent investigators (Anqi Yuan and Jialu Wu) extracted the data following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines (PRISMA) and then evaluated the quality of the eligible studies with the Newcastle-Ottawa Scale. Fixed-effects models for the meta-analyses were applied. Heterogeneity was assessed with the chi-squared (x²) test (Cochran's Q) and inconsistency index (I²). The robustness of the results was tested with the sensitivity analyses. The bias of publication was assessed with the Harbord test. Main outcomes and measures: The incidence of permanent hypothyroidism in SAT patients treated with corticosteroids or NSAIDs. Results: Our study included a total of ten comparative cohort studies with 1337 participants. We found that the incidence of developing permanent hypothyroidism in the SAT patients who received glucocorticoids treatment was significantly lower than those who received NSAIDs treatment. (OR, 0.56; 95% CI, 0.36-0.88; P = 0.01). The risk of permanent hypothyroidism in patients who received prednisone at an average initial dose < 40 mg/d was significantly lower than that in patients who received NSAIDs (OR, 0.37; 95% CI, 0.14-0.94; P = 0.04). There was no significant difference in the occurrence of permanent hypothyroidism between SAT patients who received an average initial dose ≥ 40 mg/d of prednisone and those who received only NSAIDs (OR, 0.7; 95% CI, 0.14-3.53; P = 0.67). In addition, the recurrence rate was observably higher in those receiving glucocorticoids than in those receiving NSAIDs (OR, 1.98; 95% CI, 1.12-3.5; p = 0.02). The recurrence rate was significantly higher in patients with an average initial prednisone dose of < 40 mg/d than in the NSAIDs group. There was no significant difference in the recurrence rate between patients in the mean initial prednisone dose ≥ 40 mg/d group and those in the NSAIDs group. Conclusions and relevance: In this meta-analysis, we compared the treatment outcomes of SAT patients between glucocorticoids and NSAIDs. Our results indicated that glucocorticoid treatment was associated with a lower incidence of permanent hypothyroidism than NSAID treatment. Patients treated with NSAIDs might have a lower recurrence rate. This finding might help to understand the outcome of the disease when choosing different drugs and help physicians to make appropriate decisions. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023427332.

Bone marrow adipocytes and lung cancer bone metastasis: unraveling the role of adipokines in the tumor microenvironment.

Bone is a common site of metastasis for lung cancer. The "seed and soil" hypothesis suggests that the bone marrow microenvironment ("soil") may provide a conducive survival environment for metastasizing tumor cells ("seeds"). The bone marrow microenvironment, comprising a complex array of cells, includes bone marrow adipocytes (BMAs), which constitute about 70% of the adult bone marrow volume and may play a significant role in tumor bone metastasis. BMAs can directly provide energy for tumor cells, promoting their proliferation and migration. Furthermore, BMAs participate in the tumor microenvironment's osteogenesis regulation, osteoclast(OC) regulation, and immune response through the secretion of adipokines, cytokines, and inflammatory factors. However, the precise mechanisms of BMAs in lung cancer bone metastasis remain largely unclear. This review primarily explores the role of BMAs and their secreted adipokines (leptin, adiponectin, Nesfatin-1, Resistin, chemerin, visfatin) in lung cancer bone metastasis, aiming to provide new insights into the mechanisms and clinical treatment of lung cancer bone metastasis.

The innovative design of high-performance layered transition metal oxides for sodium-ion batteries from a commercial perspective.

Sodium-ion batteries (SIBs), which have ample reserves and low production costs, are receiving more and more attention. As promising cathode candidates, layered transition metal oxides (LTMOs) have attracted intensive interest for their nontoxicity, high theoretical capacities, ease of manufacture, suitable voltage, abundant resources, and potential low cost. However, the commercial implementation of LTMOs is still hampered by their low rate capability, low energy density, insufficient cycling stability, and air instability. Therefore, this review comprehensively summarizes the research progress and modification strategies for LTMOs to enhance the stability of SIBs from microscopic heterostructure regulation to macroscale interface engineering modification. With the aim of realizing commercial applications of SIBs, more attention and research for improving the coulombic efficiency of LTMOS and close communication between academic and industrial organizations are also needed. It is expected that we will be able to provide unique perspectives for the design of powerful LTMOs in SIBs and guide the development of commercial application.

Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop.

Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid, and deep learning (DL) methods fall substantially short of either atomic accuracy or computational efficiency. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSDs of 1.77 and 1.95 Å for the CASP13+14 and CASP15 benchmark datasets, respectively, and manifests at least 2 orders of magnitude speedup in general compared with other methods. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody-antigen recognition, and drug design.

Achievement of needle-like focus by engineering radial-variant vector fields.

We present and demonstrate a novel method for engineering the radial-variant polarization on the incident field to achieve a needle of transversally polarized field without any pupil filters. We generate a new kind of localized linearly-polarized vector fields with distributions of states of polarization (SoPs) describing by the radius to the power p and explore its tight focusing, nonparaxial focusing, and paraxial focusing properties. By tuning the power p, we obtain the needle-like focal field with hybrid SoPs and give the formula for describing the length of the needle. Experimentally, we systematically investigate both the intensity distributions and the polarization evolution of the optical needle by paraxial focusing the generated vector field. Such an optical needle, which enhances the light-matter interaction, has intriguing applications in optical microma-chining and nonlinear optics.

Pituitary Apoplexy-associated Cerebral Salt Wasting Syndrome: A Case Report and Literature Review.

PURPOSE: This is a case report of a patient experiencing hyponatremia who was ultimately diagnosed with pituitary apoplexy-associated cerebral salt wasting syndrome (CSWS). METHODS: Laboratory tests, clinical evaluations, and magnetic resonance imaging were performed by specialists. FINDINGS: The patient presented with severe headache, thirst, and polyuria. Results of laboratory tests indicated hyponatremia, decreased plasma osmolality, and elevated urine osmolality. Fluid restriction worsened the situation, and normal saline treatment helped return serum sodium and chloride levels to normal. Pituitary apoplexy-associated CSWS was finally considered. IMPLICATIONS: Pituitary apoplexy is a rare but reasonable etiology for CSWS with hyponatremia. Saline therapy is usually effective.

MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pKa prediction.

Acid-base dissociation constant (pKa) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pKa prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pKa (multi-fidelity modeling with subgraph pooling for pKa prediction), a novel pKa prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pKa prediction. To overcome the scarcity of accurate pKa data, low-fidelity data (computational pKa) was used to fit the high-fidelity data (experimental pKa) through transfer learning. The final MF-SuP-pKa model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pKa achieves superior performances to the state-of-the-art pKa prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pKa achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

Learning on topological surface and geometric structure for 3D molecular generation.

Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes. The omission of these fine details leads to many models having difficulty in outputting reasonable molecules for a variety of therapeutic targets. Here, to address this challenge, we formulate a model, called SurfGen, that designs molecules in a fashion closely resembling the figurative key-and-lock principle. SurfGen comprises two equivariant neural networks, Geodesic-GNN and Geoatom-GNN, which capture the topological interactions on the pocket surface and the spatial interaction between ligand atoms and surface nodes, respectively. SurfGen outperforms other methods in a number of benchmarks, and its high sensitivity on the pocket structures enables an effective generative-model-based solution to the thorny issue of mutation-induced drug resistance.

Significant Unconventional Anomalous Hall Effect in Heavy Metal/Antiferromagnetic Insulator Heterostructures.

The anomalous Hall effect (AHE) is a quantum coherent transport phenomenon that conventionally vanishes at elevated temperatures because of thermal dephasing. Therefore, it is puzzling that the AHE can survive in heavy metal (HM)/antiferromagnetic (AFM) insulator (AFMI) heterostructures at high temperatures yet disappears at low temperatures. In this paper, an unconventional high-temperature AHE in HM/AFMI is observed only around the Néel temperature of AFM, with large anomalous Hall resistivity up to 40 nΩ cm is reported. This mechanism is attributed to the emergence of a noncollinear AFM spin texture with a non-zero net topological charge. Atomistic spin dynamics simulation shows that such a unique spin texture can be stabilized by the subtle interplay among the collinear AFM exchange coupling, interfacial Dyzaloshinski-Moriya interaction, thermal fluctuation, and bias magnetic field.

Machine learning methods for pKa prediction of small molecules: Advances and challenges.

The acid-base dissociation constant (pKa) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate pKa prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for pKa prediction, including descriptor-based and graph-based approaches, and summarize their pros and cons. Moreover, we highlight the current challenges and future directions regarding experimental data, crucial factors influencing pKa and in silico prediction tools. We hope that this review can provide a practical guidance for the follow-up studies.