TM-score Research Articles

Abstract 4122120: Prospective Implementation of a Cardiovascular Implantable Electronic Device Algorithm-Based Management Pathway in Routine Heart Failure Care

Introduction: More than 1 million patients in the US are hospitalized annually with heart failure (HF), many of whom have cardiovascular implantable electronic devices (CIEDs). CIEDs monitor physiologic variables that could identify subacute HF decompensation and impending HF hospitalization (HFH). One such algorithm is Medtronic’s TriageHF TM , which integrates nine diagnostic inputs from remote monitoring data to stratify patients into categories of low-, medium-, or high-risk of HFH in the next 30 days. Goals/Aims: To implement and evaluate a program in which two Veterans Health Administration (VHA) cardiology clinics receive high-risk TriageHF TM scores for patients with CIEDs, followed by contacting and assessing patients and then taking appropriate clinical action. Methods: From 1/18/24 to 4/19/24, TriageHF TM risk scores were calculated from both HF-specific remote transmissions that were scheduled every 30 days and unscheduled remote transmissions. Clinicians then contacted patients with high-risk scores regarding possible symptoms and provided an empiric 3-day diuretic intervention (initiation or dose augmentation), adjusted guideline-directed medical therapy (GDMT), or performed another clinical action as appropriate. Results: Among 358 patients with 1140 transmissions, 72 (6%) patients had a high-risk transmission. Mean patient age was 72.8 years, 346 (97%) were male, and 221 (62%) had a pre-existing diagnosis of heart failure. Of the 72 patients with high-risk transmissions, 67 (93%) were successfully contacted; 34 (51%) had no HF symptoms, 24 (36%) had mild to moderate symptoms, 2 (3%) had severe symptoms, 4 (6%) had non-HF symptoms, and 3 (4%) could not report symptoms. Of the 67, 57 (85%) reported adherence to medications and 17 (25%) were identified as likely having a non-HF related high-risk score. Forty-six (69%) had a clinical action taken in response to the high-risk score, including 28 (42%) with a diuretic intervention, 12 (18%) with GDMT augmented, and 25 (37%) with other actions, such as being referred to HF clinic. Conclusions: In this implementation study, clinicians contacted and assessed nearly all patients at high-risk for HF decompensation based on CIED remote monitoring data. Approximately 70% of patients had an actionable high-risk score, with approximately half prescribed empiric diuretics or augmented GDMT. A randomized clinical trial is needed to determine whether this algorithm and subsequent intervention improves clinical outcomes.

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

Diversity, Distribution and Structural Prediction of the Pathogenic Bacterial Effectors EspN and EspS.

Many Gram-negative enterobacteria translocate virulence proteins (effectors) into intestinal epithelial cells using a type III secretion system (T3SS) to subvert the activity of various cell functions possess. Many T3SS effectors have been extensively characterized, but there are still some effector proteins whose functional information is completely unknown. In this study, two predicted effectors of unknown function, EspN and EspS (Escherichia coli secreted protein N and S), were selected for analysis of translocation, distribution and structure prediction. The TEM1 (β-lactamase) translocation assay was performed, which showed that EspN and EspS are translocated into host cells in a T3SS-dependent manner during bacterial infection. A phylogenetic tree analysis revealed that homologs of EspN and EspS are widely distributed in pathogenic bacteria. Multiple sequence alignment revealed that EspN and its homologs share a conserved C-terminal region (673-1133 a.a.). Furthermore, the structure of EspN (673-1133 a.a.) was also predicted and well-defined, which showed that it has three subdomains connected by a loop region. EspS and its homologs share a sequence-conserved C-terminal (146-291 a.a.). The predicted structure of EspS (146-291 a.a.) is composed of a β-sheet consisting of four β-strands and several short helices, which has a TM score of 0.5014 with the structure of the Vibrio cholerae RTX cysteine protease domain (PDBID: 3eeb). These results suggest that EspN and EspS may represent two important classes of T3SS effectors associated with pathogen virulence, and our findings provide important clues to understanding the potential functions of EspN and EspS.

Eating competence is related to executive function skills in college students

PurposeThis study aimed to determine the relationship between eating competence (EC) and executive function (EF) skills in college students. MethodsThis cross-sectional study was part of a larger study in which an online survey was administered during autumn quarter 2020 to undergraduate students at a northwestern U.S. public university. Sociodemographic data were collected, along with measures of EC (16-item Satter Eating Competence Inventory, ecSI-2.0™) and EF skills (27-item Executive Function Index, EFI). Multiple linear regression was used to examine the ecSI-2.0™ total score and its four domain scores with each of the five EF skills. ResultsOf the 1996 respondents included in the final analyses, 40.2 % were eating competent (total ecSI-2.0™ ≥32). The mean ecSI-2.0™ score among participants was 28.7. Gender distribution was 72.0 % women, 23.3 % men, and 4.7 % trans-and-gender non-conforming (TGNC) or preferred not to answer. EFI scores were higher among women (70.9) than among men (68.2) and TGNC (64.9). Both total ecSI-2.0™ score and the contextual skills domain were significantly associated with four of the EFI subscales. Internal regulation was related to impulse control and motivational drive. Further significant associations were found with other domains of ecSI-2.0™ and specific EFI subscales. ConclusionEF skills are associated with EC in college students, particularly skills related to food management and internal regulation. Since both EF and EC can be developed, this study offers promise for future research in an increasingly gender-diverse and neurodiverse college population during a time of increasing independence and autonomy.

AI-Driven Deep Learning Techniques in Protein Structure Prediction.

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

LoCoHD: a metric for comparing local environments of proteins

Protein folds and the local environments they create can be compared using a variety of differently designed measures, such as the root mean squared deviation, the global distance test, the template modeling score or the local distance difference test. Although these measures have proven to be useful for a variety of tasks, each fails to fully incorporate the valuable chemical information inherent to atoms and residues, and considers these only partially and indirectly. Here, we develop the highly flexible local composition Hellinger distance (LoCoHD) metric, which is based on the chemical composition of local residue environments. Using LoCoHD, we analyze the chemical heterogeneity of amino acid environments and identify valines having the most conserved-, and arginines having the most variable chemical environments. We use LoCoHD to investigate structural ensembles, to evaluate critical assessment of structure prediction (CASP) competitors, to compare the results with the local distance difference test (lDDT) scoring system, and to evaluate a molecular dynamics simulation. We show that LoCoHD measurements provide unique information about protein structures that is distinct from, for example, those derived using the alignment-based RMSD metric, or the similarly distance matrix-based but alignment-free lDDT metric.

Examination of eating competence in a geo-diverse sample with metabolic syndrome

Eating competence (EatC) is an intra-individual approach to eating attitudes and behaviors associated with greater well-being. EatC research has not included persons with confirmed metabolic syndrome (MetS). Therefore, EatC of persons with MetS was explored to identify unique associations and inform implementation of MetS lifestyle interventions using baseline data from a multisite, randomized trial of a 2-year lifestyle intervention with MetS. EatC, measured with the Satter Eating Competence Inventory 2.0 (ecSI 2.0™), was examined for relationships with bioclinical measures (e.g., blood pressure, lipids), medication use, BMI, waist circumference, fruit/vegetable intake, and psychosocial factors, (e.g., stress, mindfulness). Data were collected in person and video call by trained research personnel. EatC was examined as a continuous score and as a categorical variable with ecSI 2.0™ scores ≥ 32 considered eating competent. Participants (n = 618) were predominantly female (76%), White (74%), college educated (60%). Mean age was 55.5 ± 11 y. Mean ecSI 2.0™ was 29.9 ± 7.4 and 42% were eating competent. EatC was greater for males, persons who were older and food secure. Competent eaters (vs. non-eating competent) had lower waist circumference (112.7 ± 12.5 cm vs.116.8 ± 16.0 cm; P < 0.001) and BMI (35.0 ± 6.1 vs. 37.5 ± 7.3; P < 0.001). Serum triglycerides, HDL-cholesterol, fasting blood glucose, HbA1c, and blood pressure did not differ by EatC status. Compared to non-eating competent persons, competent eaters perceived less stress, were more mindful, indicated better physical function, and more habitual vegetable intake (all P < 0.001) and sensory awareness (P < 0.05). EatC in MetS paralleled the non-MetS profile. EatC was associated with a healthier psychosocial profile, waist circumference and BMI. Findings support further research to examine the mediational or moderating influence of EatC in the treatment of MetS.

THE IMPLEMENTATION OF STORYTELLING A SHORT STORY TO IMPROVE MIDDLE SCHOOL SUNDANESE STUDENTS' SPEAKING SKILL

Speaking is one of the most important skills in learning English. However, a lot of students are good in reading, writing, and also listening but they are weak in speaking. The purpose of this study was to implement storytelling a short story in students learning process to improve students speaking skill. While storytelling is usually associated with telling a long story, even so this study used short stories. This study used a qualitative method and to perform the research, two sessions were held. The first session was to find out students prior speaking skill and the second session was held after the research was conducted to find out the effectiveness of implementing storytelling a short story to improve students speaking skill. The researchers also used observation and interview to find out students challenge in speaking English. This study involved the second grade middle school of SMP IT Nurul Ittihad. The initial findings revealed that students’ score in the second session was higher than in the first session. It can be concluded that there are improvements in students speaking skill.

FUNCTIONAL MOVEMENT SCREEN (FMSTM) AND BALANCE IN BODYBUILDERS DURING THE DEFINITION PERIOD

The functional movement and balance performances of bodybuilders and the relationship between them arouse curiosity in the definition period where intense restrictions are experienced. The aims of this study were to (a) determine functional movement screening (FMSTM) and balance performance in bodybuilders during the definition period and (b) examine the relationship between these tests. Twelve adult male bodybuilders voluntarily participated in the study. Participants performed flamingo balance test for static balance, upper &amp; lower quarter Y-balance test (YBT-LQ &amp; YBT-UQ) for dynamic balance, and FMS TM tests (deep squat, hurdle step, in-line lunge, shoulder mobility, active straight leg raise, trunk stability push-up, and rotary stability) for functional movement screening. There was a significant negative correlation between dominant leg static balance and rotary stability (r=-.832, p=.040) and a significant positive relationship between dynamic balance and rotary stability in dominant and non-dominant leg (dominant leg: r=.589, p=.044, non-dominant leg: r=.775, p=003). There was no significant relationship between YBT-UQ composite scores and FMSTM composite scores on both sides in bodybuilders during the definition period. As a result, the FMS TM score is quite low in bodybuilders during the definition period, and the increase in static and lower extremity dynamic balance performance may increase the rotary stability score. Bodybuilders are recommended to practice lower extremity static and dynamic balance exercises in addition to training during the definition period.

Cross-talk of Three Molecular Subtypes of Telomere Maintenance Defines Clinical Characteristics and Tumor Microenvironment in Gastric Cancer.

Background: Telomere maintenance takes part in the regulation of gastric cancer (GC) pathogenesis and is essential for patients' clinical features. Though the correlation between a single telomere maintenance-related gene and GC has previously been published, comprehensive exploration and systematic analysis remain to be studied. Our study is aimed at determining telomere maintenance-related molecular subtypes and examining their role in GC. Methods: By analyzing the transcriptome data, we identified three telomere maintenance-associated clusters (TMCs) with heterogeneity in clinical features and tumor microenvironment (TME). Then, we screened five prognostic telomere maintenance-related genes and established corresponding TM scores. Additionally, the expression level and biological function of tubulin beta 6 class V (TUBB6) were validated in GC tissues and cells. Results: TMC1 was correlated with EMT and TGF-beta pathway and predicted low tumor mutation burden (TMB) as well as bad prognostic outcomes. TMC3 was associated with cell cycle and DNA repair. In terms of TMB and overall survival, TMC3 exhibited opposite results against TMC1. Significant heterogeneity was observed between TMCs. TUBB6 was upregulated and could promote GC proliferation, migration, and invasion. Conclusion: Altogether, combining bioinformatics and functional experiments, we identified three molecular subtypes based on telomere maintenance-associated genes in GC, which could bring new ideas and novel biomarkers to the clinic.

Artificial Intelligence-based database forprediction ofprotein structure andtheir alterations inocular diseases.

The aim of the study is to establish an online database for predicting protein structures altered in ocular diseases by Alphafold2 and RoseTTAFold algorithms. Totally, 726 genes of multiple ocular diseases were collected for protein structure prediction. Both Alphafold2 and RoseTTAFold algorithms were built locally using the open-source codebases. A dataset with 48 protein structures from Protein Data Bank (PDB) was adopted for algorithm set-up validation. A website was built to match ocular genes with the corresponding predicted tertiary protein structures for each amino acid sequence. The predicted local distance difference test-Cα (pLDDT) and template modeling (TM) scores of the validation protein structure and the selected ocular genes were evaluated. Molecular dynamics and molecular docking simulations were performed to demonstrate the applications of the predicted structures. For the validation dataset, 70.8% of the predicted protein structures showed pLDDT greater than 90. Compared to the PDB structures, 100% of the AlphaFold2-predicted structures and 97.9% of the RoseTTAFold-predicted structure showed TM score greater than 0.5. Totally, 1329 amino acid sequences of 430 ocular disease-related genes have been predicted, of which 75.9% showed pLDDT greater than 70 for the wildtype sequences and 76.1% for the variant sequences. Small molecule docking and molecular dynamics simulations revealed that the predicted protein structures with higher confidence scores showed similar molecular characteristics with the structures from PDB. We have developed an ocular protein structure database (EyeProdb) for ocular disease, which is released for the public and will facilitate the biological investigations and structure-based drug development for ocular diseases. Database URL: http://eyeprodb.jsiec.org.

An mRNA-based reverse-vaccinology strategy to stimulate the immune response against Nipah virus in humans using fusion glycoproteins.

The zoonotic pathogen, Nipah virus, is considered a potential healthcare threat due to its high mortality rates and detrimental symptoms like encephalitis. Ribavirin, an antiviral drug helps in overcoming the number of casualties and reducing the mortality rate, but no long-lasting solution has been proposed yet putting global health security in jeopardy. Given the cognizance of mRNA-based vaccines as safe and efficacious preventative strategies against pathogens, the current study has utilized the reverse-vaccinology approach coupled with immunoinformatics to propose an mRNA-based vaccine candidate against the Nipah virus. To ensure the effectiveness of the vaccine candidate against all strains of Nipah and associated viruses, three fusion glycoproteins from Nipah and Hendra viruses were selected. A total of 30 potential epitopes, 10 B-cell-, 10 MHC-I-, and 10 MHC-II-specific, were screened for the construct. The finalized epitopes were highly antigenic with scores ranging from 0.75 to 1.7615 at a threshold of 0.4 for viruses and non-homologous to Homo sapiens eradicating any chance of immune tolerance. The construct, with a World population coverage of 97.2%, was structurally stable, thermostable, and hydrophilic with indices of 32.91, 93.62, and -0.002, respectively. The vaccine candidate's tertiary structure was predicted with a TM score of 0.131 and the refined model displayed superlative RAMA improvement (98.2) and MolProbity score (0.975). A quality factor of 93.5421% further validated the structural quality and stability. A prompt and stable immune response was also simulated, and the vaccine candidate was shown to eliminate from the body within the first five days of injection. Immune complexes count of 7000 mg/mL was predicted against the antigen with a small but nonsignificant danger signal, countered by the cytokines. Lastly, strong molecular interactions of the vaccine candidate with TLR-3 (331.09 kcal/mol) and TLR-4 (-333.31 kcal/mol) and molecular dynamics simulation analysis authenticated the immunogenic potential of the vaccine candidate. This vaccine candidate can serve as a foundation for future in-vitro and in-vivo trials to minimize or eradicate the diseases associated with the Nipah virus or the Henipaviral family.

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.

Gliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma. This study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways. The TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001). Overall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.

A computational analysis reveals eight novel high-risk single nucleotide variants of human tumor suppressor LHPP gene

BackgroundLHPP is a tumor suppressor protein associated with various malignancies like liver, oral, pharyngeal, bladder, cervical, and gastric cancers through controlling various pathways. Several single nucleotide variants have been reported to cause cancers. The main objectives of our study were to investigate the impact of the deleterious non-synonymous single nucleotide variants on structure and functions of the LHPP protein.ResultsWe used nine computational tools (SNAP2, PROVEAN, POLYPHEN 2, PREDICT SNP, MAPP, PhD-SNP, SIFT, PANTHER, and PMUT) to find out the deleterious SNPs. These nine computational algorithms predicted 34 nsSNPs to be deleterious as a result of their computational analysis. Using ConSurf, I-Mutant, SDM, MUpro, and Mutpred, we emphasized more how those harmful nsSNPs negatively affect the structure and function of the LHPP protein. Furthermore, we predicted the mutant protein structures and assessed the total energy value deviation in comparison with LHPP original structure and also calculated RMSD values and TM scores. By comparing the result from all these computational approaches, we shortlisted a total eight novel nsSNPs (D214G, D219N, Q224P, L231P, G236W, R234C, R234P, and V233G) that impose high risks to the structure and functions of LHPP protein. To analyze the mutant protein’s behavior in physiological condition, we performed 50 ns molecular dynamic simulation using WebGro online tool and found that the mutants values vary from the wild type in terms of RMSD, RMSF, Rg, SASA, and H-bond numbers. Prognostic significance analysis by Kaplan–Meier plotter showed that abnormal regulation of LHPP can also serve as a prognostic marker for the patient with breast, ovarian, and gastric cancers. Additionally, ligand binding sites analysis revealed the presence of D214G and D219N mutants in the binding site one which means these two nsSNPs can disturb the binding capacity of the LHPP protein. Protein–protein interaction analysis revealed LHPP proteins’ interactions with PPA1, ATP12A, ATP4A, ATP4B, ATP5F1, ATP5J, PPA2, ATP6V0A4, ATP6V0A2, and MT-ATP8 with different degree of connectivity.ConclusionThese results demonstrate a computational understanding of the harmful effect of nsSNPs in LHPP, which may be useful for molecular approaches.

NIS2+™, an optimisation of the blood-based biomarker NIS4® technology for the detection of at-risk NASH: A prospective derivation and validation study

NIS4® is a blood-based non-invasive test designed to effectively rule in/rule out at-risk non-alcoholic steatohepatitis (NASH), defined as non-alcoholic fatty liver disease activity score ≥4 and significant fibrosis (stage ≥2), among patients with metabolic risk factors. Robustness of non-invasive test scores across characteristics of interest including age, type 2 diabetes mellitus, and sex, and optimised analytical aspects are critical for large-scale implementation in clinical practice. We developed and validated NIS2+™, an optimisation of NIS4®, specifically designed to improve score robustness. A well-balanced training cohort (n= 198) included patients from the GOLDEN-505 trial. The validation (n= 684) and test (n= 2,035) cohorts included patients from the RESOLVE-IT trial. Well-matched subgroups were created to avoid potential confounding effects during modelling and analysis of score robustness. Models were trained using logistic regressions for at-risk NASH detection and compared using Bayesian information criteria. Performance of NIS2+™ was compared with that of NIS4®, Fibrosis-4, and alanine aminotransferase using area under the receiver operating characteristic curve, and robustness was analysed through score distribution. Using the training cohort to compare all combinations of NIS4® biomarkers, NIS2 (miR-34a-5p, YKL-40) was identified as the best combination of parameters. To correct for the sex effect on miR-34a-5p (validation cohort), sex and sex ∗ miR-34a-5p parameters were added, creating NIS2+™. In the test cohort, NIS2+™ exhibited a statistically higher area under the receiver operating characteristic curve (0.813) vs. NIS4® (0.792; p= 0.0002), Fibrosis-4 (0.653; p <0.0001), and alanine aminotransferase (0.699; p <0.0001). NIS2+™ scores were not affected by age, sex, BMI, or type 2 diabetes mellitus status, providing robust clinical performances irrespective of patient characteristics. NIS2+™ constitutes a robust optimisation of NIS4® technology for the detection of at-risk NASH. The development of non-invasive tests for accurate, large-scale detection of patients with at-risk non-alcoholic steatohepatitis (NASH; defined as NASH with non-alcoholic fatty liver disease activity score ≥4 and fibrosis stage ≥2) - who are at higher risk for disease progression and for developing liver-related life-threatening outcomes - is critical for identifying this patient population in the clinical setting and improving the screening process of NASH clinical trials. We report the development and validation of NIS2+™, a diagnostic test designed as an optimisation of NIS4® technology, a blood-based panel currently used to detect at-risk NASH in patients with metabolic risk factors. NIS2+™ showed improved performance for the detection of at-risk NASH compared with NIS4® and other non-invasive liver tests that was not impacted by patients' characteristics of interest, such as age, sex, type 2 diabetes mellitus, BMI, dyslipidaemia, and hypertension. This makes NIS2+™ a robust and reliable tool for the diagnosis of at-risk NASH among patients with metabolic risk factors, and an effective candidate for large-scale implementation in clinical practice and clinical trials.

Disordered eating and eating competence in members of online irritable bowel syndrome support groups.

This study seeks to evaluate eating competence and disordered eating likelihood among members of online support groups for irritable bowel syndrome (IBS) and determine whether eating competence and disordered eating likelihood varies according to IBS symptom severity and subtype. This cross-sectional study is based on an anonymous survey conducted from August to September 2021. Adults with IBS (N = 225) were recruited from online and social media IBS support forums. IBS symptom severity was assessed using the validated IBS Severity Scoring System (IBS-SSS), likelihood of disordered eating was assessed using the validated Eating Attitudes Test (EAT-26), and eating competence was assessed using the validated Satter Eating Competence Inventory (ecSI 2.0™). Multiple linear regression was used to predict EAT-26 total score from IBS-SSS score, age, and IBS subtype. ANOVAs were used to examine the relationships between IBS severity level, IBS subtype, and ecSI 2.0™ total score. Eating competence among the sample was low at 17% while 27% was classified as likely or very likely disordered eating. IBS severity was positively associated with EAT-26 score (p = 0.011) and ecSI 2.0™ score was significantly lower in the severe IBS group compared to the moderate IBS group (p = 0.016). No relationship was detected between IBS subtype and EAT-26 or ecSI 2.0™ scores. IBS severity was positively associated with disordered eating likelihood and negatively associated with eating competence. This sheds light on the importance of assessing eating competence and screening for disordered eating prior to selecting therapies for patients with IBS, particularly in females with severe symptoms.

Real-to-bin conversion for protein residue distances

Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. Real values than bin probabilities could more naturally represent inter-residue distances, while the latter, via spline curves more naturally helps obtain differentiable objective functions than the former. Consequently, PSP methods that exploit predicted binned distances perform better than those that exploit predicted real-valued distances. To leverage the advantage of bin probabilities in getting differentiable objective functions, in this work, we propose techniques to convert real-valued distances into distance bin probabilities. Using standard benchmark proteins, we then show that our real-to-bin converted distances help PSP methods obtain three-dimensional structures with 4%–16% better root mean squared deviation (RMSD), template modeling score (TM-Score), and global distance test (GDT) values than existing similar PSP methods. Our proposed PSP method is named real to bin (R2B) inter-residue distance predictor, and its code is available from https://gitlab.com/mahnewton/r2b.

Single-sequence protein structure prediction using supervised transformer protein language models.

Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX-Single, an automated algorithm for single-sequence protein structure prediction. It incorporates the sequence embedding from a supervised transformer protein language model into a multi-scale network enhanced by knowledge distillation to predict inter-residue two-dimensional geometry, which is then used to reconstruct three-dimensional structures via energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on orphan proteins and works well on human-designed proteins (with an average template modeling score (TM-score) of 0.79). An experimental test shows that the full trRosettaX-Single pipeline is two times faster than AlphaFold2, using much fewer computing resources (<10%). On 2,000 designed proteins from network hallucination, trRosettaX-Single generates structure models with high confidence. As a demonstration, trRosettaX-Single is applied to missense mutation analysis. These data suggest that trRosettaX-Single may find potential applications in protein design and related studies.

PRRGNVis: Multi-Level Visual Analysis of Comparison for Predicted Results of Recurrent Geometric Network

The structure of a protein determines its function, and the advancement of machine learning has led to the rapid development of protein structure prediction. Protein structure comparison is crucial for inferring the evolutionary relationship of proteins, drug discovery, and protein design. In this paper, we propose a multi-level visual analysis method to improve the protein structure comparison between predicted and actual structures. Our method takes the predicted results of the Recurrent Geometric Network (RGN) as the main research object and is mainly designed following three levels of protein structure visualization on RGN. Firstly, at the prediction accuracy level of the RGN, we use the Global Distance Test—Total Score (GDT_TS) as the evaluation standard, then compare it with distance-based root mean square deviation (dRMSD) and Template Modeling Score (TM-Score) to analyze the prediction characteristics of the RGN. Secondly, the distance deviation, torsion angle, and other attributes are used to analyze the difference between the predicted structure and the actual structure at the structural similarity level. Next, at the structural stability level, the Ramachandran Plot and PictorialBar combine to be improved to detect the quality of the predicted structure and analyze whether the amino acid residues conform to the theoretical configuration. Finally, we interactively analyze the characteristics of the RGN with the above visualization effects and give reasons and reasonable suggestions. By case studies, we demonstrate that our method is effective and can also be used to analyze other predictive network results.

Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction.

The inhibition of protein–protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein–protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2’s prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict -helices and -strands in proteins.