Early Aggregation Mechanism of SOD128-38 Based on Force Field Parameter of 5-Cyano-Tryptophan.

The aggregation of superoxide dismutase 1 (SOD1) results in amyloid deposition and is involved in familial amyotrophic lateral sclerosis, a fatal motor neuron disease. There have been extensive studies of its aggregation mechanism. Noncanonical amino acid 5-cyano-tryptophan (5-CN-Trp), which has been incorporated into the amyloid segments of SOD1 as infrared probes to increase the structural sensitivity of IR spectroscopy, is found to accelerate the overall aggregation rate and potentially modulate the aggregation process. Despite these observations, the underlying mechanism remains elusive. Here, we optimized the force field parameters of 5-CN-Trp and then used molecular dynamics simulation along with the Markov state model on the SOD128-38 dimer to explore the kinetics of key intermediates in the presence and absence of 5-CN-Trp. Our findings indicate a significantly increased probability of protein aggregate formation in 5CN-Trp-modified ensembles compared to wildtype. Dimeric ß-sheets of different natures were observed exclusively in the 5CN-Trp-modified peptides, contrasting with wildtype simulations. Free-energy calculations and detailed analyses of the dimer structure revealed augmented interstrand interactions attributed to 5-CN-Trp, which contributed more to peptide affinity than any other residues. These results explored the key events critical for the early nucleation of amyloid-prone proteins and also shed light on the practice of using noncanonical derivatives to study the aggregation mechanism.

Map conformational landscapes of intrinsically disordered proteins with polymer physics quantities.

Disordered proteins are conformationally flexible proteins that are biologically important and have been implicated in devastating diseases such as Alzheimer's disease and cancer. Unlike stably folded structured proteins, disordered proteins sample a range of different conformations that needs to be accounted for. Here, we treat disordered proteins as polymer chains, and compute a dimensionless quantity called instantaneous shape ratio (Rs), as Rs = Ree2/Rg2, where Ree is end-to-end distance and Rg is radius of gyration. Extended protein conformations tend to have high Ree compared with Rg, and thus have high Rs values, whereas compact conformations have smaller Rs values. We use a scatter plot of Rs (representing shape) against Rg (representing size) as a simple map of conformational landscapes. We first examine the conformational landscape of simple polymer models such as Random Walk, Self-Avoiding Walk, and Gaussian Walk (GW), and we notice that all protein/polymer maps lie within the boundaries of the GW map. We thus use the GW map as a reference and, to assess conformational diversity, we compute the fraction of the GW conformations (fC) covered by each protein/polymer. Disordered proteins all have high fC scores, consistent with their disordered nature. Each disordered protein accesses a different region of the reference map, revealing differences in their conformational ensembles. We additionally examine the conformational maps of the nonviral gene delivery vector polyethyleneimine at various protonation states, and find that they resemble disordered proteins, with coverage of the reference map decreasing with increasing protonation state, indicating decreasing conformational diversity. We propose that our method of combining Rs and Rg in a scatter plot generates a simple, meaningful map of the conformational landscape of a disordered protein, which in turn can be used to assess conformational diversity of disordered proteins.

Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Comprehensive Comparison and Critical Assessment of RNA-Specific Force Fields.

Molecular dynamics simulations play a pivotal role in elucidating the dynamic behaviors of RNA structures, offering a valuable complement to traditional methods such as nuclear magnetic resonance or X-ray. Despite this, the current precision of RNA force fields lags behind that of protein force fields. In this work, we systematically compared the performance of four RNA force fields (ff99bsc0χOL3, AMBERDES, ff99OL3_CMAP1, AMBERMaxEnt) across diverse RNA structures. Our findings highlight significant challenges in maintaining stability, particularly with regard to cross-strand and cross-loop hydrogen bonds. Furthermore, we observed the limitations in accurately describing the conformations of nonhelical structural motif, terminal nucleotides, and also base pairing and base stacking interactions by the tested RNA force fields. The identified deficiencies in existing RNA force fields provide valuable insights for subsequent force field development. Concurrently, these findings offer recommendations for selecting appropriate force fields in RNA simulations.

Discovery of sesquiterpene from Youngia japonica with antitumor effect.

Fourteen sesquiterpenes, including one undescribed sesquiterpene lactone, were isolated from Youngia japonica, and their structures were identified by NMR, HRESIMS, ECD and calculated ECD. Cytotoxic activities of all isolates against A549, HeLa, and 4 T1 cell lines were detected by CCK8 assay. Among them, 2 showed obvious cytotoxic activity against A549 cells. Subsequently, the production of ROS, and apoptosis of A549 cells treated with 2 were evaluated. The result showed that 2 distinctly increased the ROS level, and induced the apoptosis of A549 cells. Further anticancer mechanism studies showed that 2 increased the expression of cleaved caspase 3. Taken together, our results demonstrated that 2 might become potential leading compounds for the treatment of lung cancer.

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.

MOTIVATION: Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial role, and numerous successful methods have been developed. Notably, deep learning-based protein sequence design methods have experienced significant advancements in recent years. However, a comprehensive and systematic comparison and evaluation of these methods have been lacking, with indicators provided by different methods often inconsistent or lacking effectiveness. RESULTS: To address this gap, we have designed a diverse set of indicators that cover several important aspects, including sequence recovery, diversity, root-mean-square deviation of protein structure, secondary structure, and the distribution of polar and nonpolar amino acids. In our evaluation, we have employed an improved weighted inferiority-superiority distance method to comprehensively assess the performance of eight widely used deep learning-based protein sequence design methods. Our evaluation not only provides rankings of these methods but also offers optimization suggestions by analyzing the strengths and weaknesses of each method. Furthermore, we have developed a method to select the best temperature parameter and proposed solutions for the common issue of designing sequences with consecutive repetitive amino acids, which is often encountered in protein design methods. These findings can greatly assist users in selecting suitable protein sequence design methods. Overall, our work contributes to the field of protein sequence design by providing a comprehensive evaluation system and optimization suggestions for different methods.

Four undescribed coumarin derivatives, with ten amides from the roots of Ficus hirta and their cytotoxic activities.

Four undescribed coumarin derivatives, ficusalt A (1) and ficusalt B (2), a pair of racemic coumarins, (±) ficudimer A (3a/3b), along with ten known amides, were isolated from the roots of Ficus hirta. Their structures were elucidated by several spectroscopic data analyses, including HRESIMS, NMR, and X-ray single-crystal diffraction. The cytotoxic activities of all compounds against HeLa, HepG2, MCF-7, and H460 cell lines were detected using the MTT assay. Among these, 5 showed the highest activity against HeLa cells. Subsequently, the apoptotic, anti-invasive, and anti-migration effects of 5 on HeLa cells were determined by flow cytometer, transwell invasion assay, and wound-healing assay, respectively. The result suggested that 5 distinctly induced the apoptosis in HeLa cells and inhibited their invasion and migration. Further studies on anticancer mechanisms were conducted using Western blotting. As a result, 5 increased the cleavage of PARP and the expression of pro-apoptotic protein Bax. Moreover, 5 notably upregulated the phosphorylation of p38 and JNK, whereas inhibited the expression of p-ERK and p-AKT. Our results demonstrated that 5 could be a potential leading compound for further application in the treatment of cervical cancer.

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling.

The biological function of proteins is determined not only by their static structures but also by the dynamic properties of their conformational ensembles. Numerous high-accuracy static structure prediction tools have been recently developed based on deep learning; however, there remains a lack of efficient and accurate methods for exploring protein dynamic conformations. Traditionally, studies concerning protein dynamics have relied on molecular dynamics (MD) simulations, which incur significant computational costs for all-atom precision and struggle to adequately sample conformational spaces with high energy barriers. To overcome these limitations, various enhanced sampling techniques have been developed to accelerate sampling in MD. Traditional enhanced sampling approaches like replica exchange molecular dynamics (REMD) and frontier expansion sampling (FEXS) often follow the MD simulation approach and still cost a lot of computational resources and time. Variational autoencoders (VAEs), as a classic deep generative model, are not restricted by potential energy landscapes and can explore conformational spaces more efficiently than traditional methods. However, VAEs often face challenges in generating reasonable conformations for complex proteins, especially intrinsically disordered proteins (IDPs), which limits their application as an enhanced sampling method. In this study, we presented a novel deep learning model (named Phanto-IDP) that utilizes a graph-based encoder to extract protein features and a transformer-based decoder combined with variational sampling to generate highly accurate protein backbones. Ten IDPs and four structured proteins were used to evaluate the sampling ability of Phanto-IDP. The results demonstrate that Phanto-IDP has high fidelity and diversity in the generated conformation ensembles, making it a suitable tool for enhancing the efficiency of MD simulation, generating broader protein conformational space and a continuous protein transition path.

Synthesis and degradation of the cyclic dinucleotide messenger c-di-AMP in the hyperthermophilic archaeon Pyrococcus yayanosii.

Cyclic di-adenosine monophosphate (c-di-AMP) is a newly identified prokaryotic cyclic dinucleotide second messenger well elucidated in bacteria, while less studied in archaea. Here, we describe the enzymes involved in c-di-AMP metabolism in the hyperthermophilic archaeon Pyrococcus yayanosii. Our results demonstrate that c-di-AMP is synthesized from two molecules of ATP by diadenylate cyclase (DAC) and degraded into pApA and then to AMP by a DHH family phosphodiesterase (PDE). DAC can be activated by a wider variety of ions, using two conserved residues, D188 and E244, to coordinate divalent metal ions, which is different from bacterial CdaA and DisA. PDE possesses a broad substrate spectrum like bacterial DHH family PDEs but shows a stricter base selection between A and G in cyclic dinucleotides hydrolysis. PDE shows differences in substrate binding patches from bacterial counterparts. C-di-AMP was confirmed to exist in Thermococcus kodakarensis cells, and the deletion of the dac or pde gene supports that the synthesis and degradation of c-di-AMP are catalyzed by DAC and PDE, respectively. Our results provide a further understanding of the metabolism of c-di-AMP in archaea.

Dysambiol, an Anti-inflammatory Secomeroterpenoid from a Dysidea sp. Marine Sponge.

An unusual secomeroterpenoid, dysambiol (1), was isolated from a Dysidea sp. marine sponge collected from the South China Sea. Dysambiol features an unprecedented secomeroterpene scaffold with a rare lactone bridge. The structure of 1 was determined by extensive spectroscopic analysis, Mosher's method, and electronic circular dichroism calculation. Dysambiol displayed potent anti-inflammatory activity in LPS-induced Raw 264.7 macrophages by regulating the NF-κB/MPAK signaling pathway.

Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins.

Intrinsically disordered proteins (IDPs) play a critical role in many biological processes. Due to the inherent structural flexibility of IDPs, experimental methods present significant challenges for sampling their conformational information at the atomic level. Therefore, molecular dynamics (MD) simulations have emerged as the primary tools for modeling IDPs whose accuracy depend on force field and water model. To enhance the accuracy of physical modeling of IDPs, several force fields have been developed. However, current water models lack precision and underestimate the interaction between water molecules and proteins. Here, we used Monte-Carlo re-weighting method to re-parameterize a three-point water model based on OPC3 for IDPs (named OPC3-B). We benchmarked the performance of OPC3-B compared with nine different water models for 10 IDPs and three ordered proteins. The results indicate that the performance of OPC3-B is better than other water models for both IDPs and ordered proteins. At the same time, OPC3-B possess the power of transferability with other force field to simulate IDPs. This newly developed water model can be used to insight into the research of sequence-disordered-function paradigm for IDPs.

Anti-osteoporotic drugs affect the pathogenesis of gut microbiota and its metabolites: a clinical study.

Background: Disordered gut microbiota (GM) structure and function may contribute to osteoporosis (OP). This study explores how traditional Chinese medicine (TCM) intervention affects the structure and function of the GM in patients with OP. Method: In a 3-month clinical study, 43 patients were randomly divided into two groups receiving conventional treatment and combined TCM (Yigu decoction, YGD) treatment. The correlation between the intestinal flora and its metabolites was analyzed using 16S rDNA and untargeted metabolomics and the combination of the two. Results: After three months of treatment, patients in the treatment group had better bone mineral density (BMD) than those in the control group (P < 0.05). Patients in the treatment group had obvious abundance changes in GM microbes, such as Bacteroides, Escherichia-Shigella, Faecalibacterium, Megamonas, Blautia, Klebsiella, Romboutsia, Akkermansia, and Prevotella_9. The functional changes observed in the GM mainly involved changes in metabolic function, genetic information processing and cellular processes. The metabolites for which major changes were observed were capsazepine, Phe-Tyr, dichlorprop, D-pyroglutamic acid and tamsulosin. These metabolites may act through metabolic pathways, the citrate cycle (TCA cycle) and beta alanine metabolism. Combined analysis showed that the main acting metabolites were dichlorprop, capsazepine, D-pyroglutamic acid and tamsulosin. Conclusion: This study showed that TCM influenced the structure and function of the GM in patients with OP, which may be one mechanism by which TCM promotes the rehabilitation of patients with OP through the GM.

Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions.

Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.

Metabolic Signature of Healthy Lifestyle and Risk of Rheumatoid Arthritis: Observational and Mendelian Randomization Study.

BACKGROUND: Although substantial evidence reveals that healthy lifestyle behaviors are associated with a lower risk of rheumatoid arthritis (RA), the underlying metabolic mechanisms remain unclear. OBJECTIVES: This study aimed to identify the metabolic signature reflecting a healthy lifestyle and investigate its observational and genetic linkage with RA risk. METHODS: This study included 87,258 UK Biobank participants (557 cases with incident RA) aged 37-73 y with complete lifestyle, genotyping, and nuclear magnetic resonance (NMR) metabolomics data. A healthy lifestyle was assessed based on 5 factors: healthy diet, regular exercise, not smoking, moderate alcohol consumption, and normal body mass index. The metabolic signature was developed by summing the selected metabolites' concentrations weighted by the coefficients using elastic net regression. We used the multivariate Cox model to assess the associations between metabolic signatures and RA risk, and examined the mediating role of the metabolic signature in the impact of a healthy lifestyle on RA. We performed genome-wide association analysis (GWAS) to obtain genetic variants associated with the metabolic signature and then conducted Mendelian randomization (MR) analyses to detect causality. RESULTS: The metabolic signature comprised 81 metabolites, robustly correlated with a healthy lifestyle (r = 0.45, P = 4.2 × 10-15). The metabolic signature was inversely associated with RA risk (HR per standard deviation (SD) increment: 0.76; 95% CI: 0.70-0.83), and largely explained the protective effects of healthy lifestyle on RA with 64% (95% CI: 50.4-83.3) mediation proportion. 1- and 2-sample MR analyses also consistently showed the associations of genetically inferred per SD increment in metabolic signature with a reduction in RA risk (HR: 0.84; 95% CI: 0.75-0.94; and P = 0.002 and OR: 0.84; 95% CI: 0.73-0.97; and P = 0.02, respectively). CONCLUSIONS: Our findings implicate that the metabolic signature reflecting healthy lifestyle is a potential causal mediator in the development of RA, highlighting the importance of early lifestyle intervention and metabolic status tracking for precise prevention of RA.

Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as potential drugs with high selectivity and safety. However, current experimental methods have limitations to identify allosteric sites. Therefore, molecular dynamics simulation based on empirical force field becomes an important complement of experimental methods. Moreover, the precision and efficiency of current force fields need improvement. Deep learning and reweighting methods were used to train allosteric protein-specific precise force field (named APSF). Multiple allosteric proteins were used to evaluate the performance of APSF. The results indicate that APSF can capture different types of allosteric pockets and sample multiple energy-minimum reference conformations of allosteric proteins. At the same time, the efficiency of conformation sampling for APSF is higher than that for ff14SB. These findings confirm that the newly developed force field APSF can be effectively used to identify the allosteric pocket that can be further used to screen potential allosteric drugs based on these pockets.

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.

Intrinsically disordered proteins (IDPs) account for more than 50% of the human proteome and are closely associated with tumors, cardiovascular diseases, and neurodegeneration, which have no fixed three-dimensional structure under physiological conditions. Due to the characteristic of conformational diversity, conventional experimental methods of structural biology, such as NMR, X-ray diffraction, and CryoEM, are unable to capture conformational ensembles. Molecular dynamics (MD) simulation can sample the dynamic conformations at the atomic level, which has become an effective method for studying the structure and function of IDPs. However, the high computational cost prevents MD simulations from being widely used for IDPs conformational sampling. In recent years, significant progress has been made in artificial intelligence, which makes it possible to solve the conformational reconstruction problem of IDP with fewer computational resources. Here, based on short MD simulations of different IDPs systems, we use variational autoencoders (VAEs) to achieve the generative reconstruction of IDPs structures and include a wider range of sampled conformations from longer simulations. Compared with the generative autoencoder (AEs), VAEs add an inference layer between the encoder and decoder in the latent space, which can cover the conformational landscape of IDPs more comprehensively and achieve the effect of enhanced sampling. Through experimental verification, the Cα RMSD between VAE-generated and MD simulation sampling conformations in the 5 IDPs test systems was significantly lower than that of AE. The Spearman correlation coefficient on the structure was higher than that of AE. VAE can also achieve excellent performance regarding structured proteins. In summary, VAEs can be used to effectively sample protein structures.

Multi-networks connectivity at baseline predicts the clinical efficacy of left angular gyrus-navigated rTMS in the spectrum of Alzheimer's disease: A sham-controlled study.

INTRODUCTION: Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is effective in alleviating cognitive deficits in Alzheimer's disease (AD). However, the strategy for target determination and the mechanisms for cognitive improvement remain unclear. METHODS: One hundred and thirteen elderly subjects were recruited in this study, including both cross-sectional (n = 79) and longitudinal experiments (the rTMS group: n = 24; the sham group: n = 10). The cross-sectional experiment explored the precise intervention target based on the cortical-hippocampal network. The longitudinal experiment investigated the clinical efficacy of neuro-navigated rTMS treatment over a four-week period and explored its underlying neural mechanism using seed-based and network-based analysis. Finally, we applied connectome-based predictive modeling to predict the rTMS response using these functional features at baseline. RESULTS: RTMS at a targeted site of the left angular gyrus (MNI: -45, -67, 38) significantly induced cognitive improvement in memory and language function (p < 0.001). The improved cognition correlated with the default mode network (DMN) subsystems. Furthermore, the connectivity patterns of DMN subsystems (r = 0.52, p = 0.01) or large-scale networks (r = 0.85, p = 0.001) at baseline significantly predicted the Δ language cognition after the rTMS treatment. The connectivity patterns of DMN subsystems (r = 0.47, p = 0.019) or large-scale networks (r = 0.80, p = 0.001) at baseline could predict the Δ memory cognition after the rTMS treatment. CONCLUSION: These findings suggest that neuro-navigated rTMS targeting the left angular gyrus could improve cognitive function in AD patients. Importantly, dynamic regulation of the intra- and inter-DMN at baseline may represent a potential predictor for favorable rTMS treatment response in patients with cognitive impairment.

Severe wound infection by MRCNS following bilateral inguinal herniorrhaphy.

BACKGROUND: Wound infection after inguinal hernia surgery is not uncommon in the clinical setting. The common microbial aetiology of postoperative inguinal hernia wound infection is Gram-positive bacteria. Staphylococcus aureus is a common pathogen causing wound infection while Staphylococcus epidermidis and Pseudomonas are rare. Staphylococcus epidermidis as a cause of severe wound infection is rarely described in literature. We herein present a case of a 79-year-old man with a rare wound infection after bilateral inguinal herniorrhaphy caused by MRCNS (Methicillin Resistant Coagulase Negative Staphylococcus). CASE PRESENTATION: We present a case of wound infection accompanied by fever with a temperature of 38.8 °C after bilateral inguinal herniorrhaphy in a 79-year-old man. Bilateral inguinal wounds were marked by redness and swelling, with skin necrosis. In addition, an abscess of approximately 1.5 cm × 1.5 cm was seen on the left wrist. A small amount of gas under the skin in the wound area was observed after pelvic computed tomography (CT) scans. No bacteria were cultured from the inguinal wound discharge, while blood culture detected MRCNS, and Acinetobacter lwoffi was cultured from the pus in the left wrist. We chose appropriate antibiotics based on the results of the bacterial culture and the drug susceptibility results. Vacuum assisted closure (VAC) therapy was used after debridement. The patient was discharged after the wounds improved. He was followed up for ten months and showed no signs of complications. We are sharing our experience along with literature review. CONCLUSIONS: We are presenting a rare case of MRCNS wound infection following open inguinal hernia surgery. Although a rarity, clinicians performing inguinal hernia surgery must consider this entity in an infected wound and follow up the patient for complications of MRCNS.

Phosphorylation Modification Force Field FB18CMAP Improving Conformation Sampling of Phosphoproteins.

Phosphorylation of proteins plays an important regulatory role at almost all levels of cellular organization. Molecular dynamics (MD) simulation is a promising tool to reveal the mechanism of how phosphorylation regulates many key biological processes at the atomistic level. MD simulation accuracy depends on force field precision, while the current force fields for phospho-amino acids have resulted in notable inconsistency with experimental data. Here, a new force field parameter (named FB18CMAP) is generated by fitting against quantum mechanics (QM) energy in aqueous solution with φ/ψ dihedral potential-energy surfaces optimized using CMAP parameters. MD simulations of phosphorylated dipeptides, intrinsically disordered proteins (IDPs), and ordered (folded) proteins show that FB18CMAP can mimic NMR observables and structural characteristics of phosphorylated dipeptides and proteins more accurately than the FB18 force field. These findings suggest that FB18CMAP performs well in both the simulation of ordered and disordered states of phosphorylated proteins.

Phosphorylation Regulation Mechanism of ß2 Integrin for the Binding of Filamin Revealed by Markov State Model.

Leukocyte adhesion deficiency-1 (LAD-1) disorder is a severe immunodeficiency syndrome caused by deficiency or mutation of ß2 integrin. The phosphorylation on threonine 758 of ß2 integrin acts as a molecular switch inhibiting the binding of filamin. However, the switch mechanism of site-specific phosphorylation at the atom level is still poorly understood. To resolve the regulation mechanism, all-atom molecular dynamics simulation and Markov state model were used to study the dynamic regulation pathway of phosphorylation. Wild type system possessed lower binding free energy and fewer number of states than the phosphorylated system. Both systems underwent local disorder-to-order conformation conversion when achieving steady states. To reach steady states, wild type adopted less number of transition paths/shortest path according to the transition path theory than the phosphorylated system. The underlying phosphorylated regulation pathway was from P1 to P0 and then P4 state, and the main driving force should be hydrogen bond and hydrophobic interaction disturbing the secondary structure of phosphorylated states. These studies will shed light on the pathogenesis of LAD-1 disease and lay a foundation for drug development.