PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Prediction Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗
PubMed2026

Identification of MHC Ligands Through Allele-Guided Isolation Combined With Machine Learning for Improved MHC Assignment Using ARDisplay-I.

The isolation of major histocompatibility complex (MHC) ligands and subsequent analysis by mass spectrometry is considered the gold standard for defining targets for T cell-based immunotherapies. However, as many targets of high tumor specificity are only presented at low abundance on the cell surface of tumor cells, the efficient isolation of these peptides is crucial for their successful detection. Here, we demonstrate how optimizing the MHC ligand isolation strategy, based on both the presenting MHC alleles and the individual peptide level, enhances the identification of specific MHC ligands. This ideally acknowledges not only the hydrophobicity but also the post-translational modifications of the respective MHC ligands. To further improve the identification and characterization of MHC ligands, we developed an MHC class I ligand prediction algorithm (ARDisplay-I) that outperforms current state-of-the-art tools when benchmarked against competitors such as netMHCpan 4.1, MixMHCpred, or MHCflurry. Implementing these strategies can augment the development of T cell receptor-based therapies by improving the identification of novel immunotherapy targets and enriching the resources available in the computational immunology field through a superior MHC presentation prediction algorithm.

Ligands↗
PubMed2026

Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study.

BACKGROUND: The predictD intervention, delivered by family physicians (FPs), has demonstrated effectiveness and cost-efficiency in preventing depression and anxiety. The e-predictD study aims to design, develop, and evaluate a novel personalized intervention for depression prevention by integrating information and communication technologies (ICTs), risk prediction algorithms, and decision support systems (DSS) for both patients and FPs. OBJECTIVE: To evaluate the satisfaction, usability, and acceptability, of a beta version of the e-predictD intervention in primary care settings. METHODS: The e-predictD intervention follows a biopsychosocial approach, including an initial patient-FP interview, specific FP training, and an app. A β-version was tested in a pilot study without a control group over three months. The app integrates a validated depression risk prediction algorithm, decision algorithms, and a monitoring system supporting the DSS. The DSS generates a personalized prevention plan (PPP) from eight intervention modules: physical exercise, social relationships, problem-solving, communication skills, decision-making, assertiveness, sleep improvement, and cognitive restructuring. Patients and FPs discussed the PPP in a 15-minute baseline interview, selecting modules for implementation over three months. Semi-structured interviews gathered feedback. Assessments included depression (PHQ-9), anxiety (GAD-7), quality of life (SF-12), and major depression risk (predictD algorithm). RESULTS: Six FPs from six Spanish cities enrolled 56 non-depressed patients at moderate-to-high risk of depression; 47 (84%) completed follow-up. The app was used for a median of six days (interquartile range: 1-30). Both FPs and patients expressed satisfaction, leading to incorporated improvements. After three months, significant reductions in major depression risk and anxiety symptoms were observed, alongside improved mental quality of life. However, no significant changes were found in depressive symptoms or physical quality of life. CONCLUSION: This pilot study supports the feasibility and acceptability of the e-predictD β-version, despite lower-than-expected app usability. Health improvements were observed, warranting confirmation in a randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT03990792.

Adult↗
PubMed1975

Assessment of some problems associated with prediction of the three-dimensional structure of a protein from its amino-acid sequence.

It is shown that most present empirical prediction algorithms provide information about the conformational states of individual residues, but give little information about the three-dimensional structure of a protein. It is necessary to predict the conformational state of every residue before the resulting structure can serve as a starting conformation to compute the native structure. It is also shown that even a perfect five-state algorithm (which does not include long-range interactions from disulifide loop closing or solvation) will not lead to a globular structure resembling the native one. However, starting from the results of a perfect prediction algorithm, it appears that conformational energy minimization (with long-range interactions included) can lead to a structure having the general features of the native protein.

Amino Acid Sequence↗
PubMed2025

Study research protocol for Phenome India-CSIR Health Cohort Knowledgebase: A prospective multi-modal follow-up study on a nationwide employee cohort.

Predicting individual health trajectories based on risk scores can help formulate effective preventive strategies for diseases and their complications. Currently, most risk prediction algorithms rely on epidemiological data from the Caucasian population, which often do not translate well to the Indian population due to ethnic diversity, differing dietary and lifestyle habits, and unique risk profiles. In this multi-center prospective longitudinal study conducted across India, we aim to address these challenges by developing clinically relevant risk prediction scores for cardio-metabolic diseases specifically tailored to the Indian population. India, which accounts for nearly 18% of the global population, also has a significant diaspora worldwide. This program targets longitudinal collection and bio-banking of samples from over 10 000 employees both working and retirees of the Council of Scientific and Industrial Research and their spouses, with baseline sample collection already completed. During the baseline collection, we gathered multi-parametric data including clinical questionnaires, lifestyle and dietary habits, anthropometric parameters, lung function assessments, liver elastography by Fibroscan, electrocardiogram readings, biochemical data, and molecular assays, including but not limited to genomics, plasma proteomics, metabolomics, and fecal microbiome analysis. In addition to exploring associations between these parameters and their cardio-metabolic outcomes, we plan to employ artificial intelligence algorithms to develop predictive models for phenotypic conditions. This study could pave the way for precision medicine tailored to the Indian population, particularly for the middle-income strata, and help refine the normative values for health and disease indicators in India.

cardio-metabolic↗
PubMed1976

An assessment of protein secondary structure prediction methods based on amino acid sequence.

Five of the several secondary structure prediction methods based on protein amino acid sequence has been computerized, allowing the calculation of joint prediction histograms which have been shown to be superior to any individual prediction. The known structures of about 40 proteins experimentally determined by X-ray crystallography are compared with the predictions resulting from calculated histograms. The accuracy of the predictions for helices is generally much better than for both beta-sheet regions and for turns. The overall agreement between prediction and observation within the amino terminal half of the protein molecules is clearly superior to that for the carboxyl half, suggesting an amino nucleating core. Predictions for smaller proteins and thermally stable proteins are generally good, indicating the sensitivity of the methods to short-range but not long-range interactions. In less than half the cases tested were the predictions useful; there was no way of knowing ahead of time if a favorable prediction would result. Given the lack of dramatic improvement with an increase in data base for the schemes and the generally poor agreement factors, it appears that a perfect predictive algorithm must include a consideration of energy minimization, thermalization, and long-range interactions. Extreme caution is suggested in applying present prediction routines to unknown protein structures.

Amino Acid Sequence↗
PubMed2026

Tackling non-canonical splicing in arrhythmogenic cardiomyopathy to reduce the uncertain significance variants burden.

BACKGROUND: Splice-altering variants (SAVs), particularly those outside canonical splice sites, are an underappreciated contributor to inherited cardiovascular diseases. In arrhythmogenic cardiomyopathy (ACM), these variants frequently remain classified as of uncertain significance (VUS) due to limited predictive power and lack of transcript-level evidence, constraining genetic yield and clinical management. Our study aimed to determine the functional impact of SAVs in ACM genes and refine their classification using ACMG/AMP and ClinGen SVI criteria. METHODS: SAVs identified in 200 ACM probands underwent SpliceAI prediction, GTEx cardiac exon-usage annotation, and functional assessment using pSPL3-based minigene assays. Aberrant transcripts were quantified using Percent Splicing Alteration (PSA). Segregation data and ACMG/AMP criteria refined by ClinGen SVI were applied to integrate functional and clinical evidence for classification. RESULTS: Aberrant splicing was confirmed in 9/20 variants (45%), including synonymous, missense, and non-canonical intronic changes. SpliceAI scores correlated strongly with PSA values (R²=0.86). Case-control burden testing revealed significant enrichment of splice-altering variants in DSP, DSG2, DSC2 and FLNC. Integrating predictive algorithms with experimental validation and segregation analysis markedly enhances reclassification of 16/20 variants (80%). CONCLUSION: Splicing defects beyond canonical sites significantly shape ACM genetic landscape. Integrating predictive models with experimental validation clarifies uncertain variants bridging the gap between genomic uncertainty and clinical decision-making.

Humans↗
PubMed2026

Deciphering the Role of LNX2 as a Potential Contributor to Neurodevelopmental Disorders.

BACKGROUND/OBJECTIVES: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition characterized by a complex and multifactorial genetic architecture. In this study, we report a male patient, born to non-consanguineous healthy parents, presenting with ADHD and oppositional defiant disorder (ODD). METHODS: Trio-based whole-exome sequencing (WES) was performed in the proband and both parents. Variant classification was performed according to American College of Medical Genetics and Genomics (ACMG) guidelines, and the potential pathogenicity of the identified variant was further assessed through multiple in silico prediction algorithms and protein structural analyses. RESULTS: WES identified a homozygous variant in the LNX2 gene (NM_153371.4: c.1165G>A, p.Ala389Thr), classified as a variant of uncertain significance (VUS) and supported by multiple in silico predictions. LNX2 is expressed during brain development and encodes an E3 ubiquitin ligase involved in neuronal differentiation and synaptic function. The identified variant is located within the PDZ2 domain, a functionally relevant region involved in protein-protein interactions. Although the variant is reported in population databases (gnomAD ID: rs148429804), it has not been associated with any clinical phenotype, and its presence in the homozygous state has been reported only once, remaining extremely rare and lacking clinical annotation. Structural modelling predicted localized rearrangement of the hydrogen-bonding network within the PDZ2 domain without major conformational changes. Integrative transcriptomic, and single-cell analyses further supported the biological relevance of LNX2 in neurodevelopment, highlighting its preferential association with neuronal projection-cell networks, synaptic vesicle trafficking pathways, and neuron-specific regulatory programs. CONCLUSION: Although the identified LNX2 variant cannot be considered causative for the patient's phenotype and a definitive disease-gene relationship cannot be established based on a single individual, the complementary genetic, structural, and transcriptomic findings support the biological plausibility of LNX2 as a candidate gene for neurodevelopmental disorders. Additional independent patients and functional studies will be required to clarify its contribution to human disease.

Child↗
PubMed2025

Unraveling the complex genetic landscape of OTOF-related hearing loss: a deep dive into cryptic variants and haplotype phasing.

BACKGROUND: Pathogenic variants in OTOF are a major cause of auditory synaptopathy. However, challenges remain in interpreting OTOF variants, including difficulties in confirming haplotype phasing using traditional short-read sequencing (SRS) due to the large gene size, the potential incomplete penetrance of certain variants, and difficulties in assessing variants at non-canonical splice sites. This study aims to revisit the genetic landscape of OTOF variants in a Taiwanese non-syndromic auditory neuropathy spectrum disorder (ANSD) cohort using a combination of sequencing technologies, predictive tools, and experimental validations. METHODS: We performed SRS to analyze OTOF variants in 65 unrelated Taiwanese patients diagnosed with non-syndromic ANSD, complemented by long-read sequencing (LRS) for haplotype phasing. A prediction-to-validation pipeline was implemented to assess the pathogenicity of cryptic variants using SpliceAI software and minigene assays. RESULTS: Biallelic pathogenic OTOF variants were identified in 33 patients (50.8%), while monoallelic variants were found in five patients. Three novel variants, c.3864G > A (p.Ala1288 =), c.4501G > A (p.Ala1501Thr), and c.5813 + 2T > C, were detected. The pathogenicity of two non-canonical mis-splicing variants, c.3894 + 5G > C and c.3864G > A (p.Ala1288 =), was confirmed by minigene assays. LRS-based haplotype phasing revealed that the common missense variant c.5098G > C (p.Glu1700Gln) and the novel variant c.5975A > G (p.Lys1992Arg) are in cis and form a founder pathogenic allele in the Taiwanese population. CONCLUSIONS: Our study highlights the genetic heterogeneity of DFNB9 and emphasizes the importance of population-specific variant interpretation. The integration of advanced sequencing technologies, predictive algorithms, and functional validation assays will improve the accuracy of molecular diagnosis and inform personalized treatment strategies for individuals with DFNB9.

Humans↗
PubMed1992

Isolation and cloning of Omp alpha, a coiled-coil protein spanning the periplasmic space of the ancestral eubacterium Thermotoga maritima.

We have discovered a new oligomeric protein component associated with the outer membrane of the ancestral eubacterium Thermotoga maritima. In electron micrographs, the protein, Omp alpha, appears as a rod-shaped spacer that spans the periplasm, connecting the outer membrane to the inner cell body. Purification, biochemical characterization and sequencing of Omp alpha suggest that it is a homodimer composed of two subunits of 380 amino acids with a calculated M(r) of 43,000 and a pI of 4.54. The sequence of the omp alpha gene indicates a tripartite organization of the protein with a globular NH2-terminal domain of 64 residues followed by a putative coiled-coil segment of 300 residues and a COOH-terminal, membrane-spanning segment. The predicted length of the coiled-coil segment (45 nm) correlates closely with the spacing between the inner and outer membranes. Despite sequence similarity to a large number of coiled-coil proteins and high scores in a coiled-coil prediction algorithm, the sequence of the central rod-shaped domain of Omp alpha does not have the typical 3.5 periodicity of coiled-coil proteins but rather has a periodicity of 3.58 residues. Such a periodicity was also found in the central domain of staphylococcal M protein and beta-giardin and might be indicative of a subclass of fibrous proteins with packing interactions that are distinct from the ones seen in other two-stranded coiled-coils.

Amino Acid Sequence↗
PubMed1992

A structural assessment of the apo[a] protein of human lipoprotein[a].

Apolipoprotein[a], the highly glycosylated, hydrophilic apoprotein of lipoprotein[a] (Lp[a]), is generally considered to be a multimeric homologue of plasminogen, and to exhibit atherogenic/thrombogenic properties. The cDNA-inferred amino acid sequence of apo[a] indicates that apo[a], like plasminogen and some zymogens, is composed of a kringle domain and a serine protease domain. To gain insight into possible positive functions of Lp[a], we have examined the apo[a] primary structure by comparing its sequence with those of other proteins involved in coagulation and fibrinolysis, and its secondary structure by using a combination of structure prediction algorithms. The kringle domain encompasses 11 distinct types of repeating units, 9 of which contain 114 residues. These units, called kringles, are similar but not identical to each other or to PGK4. Each apo[a] kringle type was compared with kringles which have been shown to bind lysine and fibrin, and with bovine prothrombin kringle 1. Apo[a] kringles are linked by serine/threonine- and proline-rich stretches similar to regions in immunoglobulins, adhesion molecules, glycoprotein Ib-alpha subunit, and kininogen. In comparing the protease domains of apo[a] and plasmin, apo[a] contains a region between positions 4470 and 4492 where 8 substitutions, 9 deletions, and 1 insertion are apparent. Our analysis suggests that apo[a] kringle-type 10 has a high probability of binding to lysine in the same way as PGK4. In the only human apo[a] polymorph sequenced to date, position 4308 is occupied by serine, whereas the homologous position in plasmin is occupied by arginine and is an important site for proteolytic cleavage and activation. An alternative site for the proteolytic activation of human apo[a] is proposed.

Amino Acid Sequence↗
PubMed2026

Circular RNAs orchestrate integrated post-transcriptional responses to combined heat and drought stress in rice.

Circular RNAs (circRNAs) are emerging post-transcriptional regulators, yet their landscape and functional roles in rice under combined abiotic stress remain largely unexplored. Here, we systematically reanalyzed strand-specific RNA-seq data to characterize circRNAs responsive to simultaneous heat and drought stress. Following quality control, read mapping, and dual-algorithm prediction using CIRI2 and CIRCexplorer2, we identified 208 high-confidence circRNAs distributed across all 12 chromosomes. Comparative profiling revealed 83 circRNAs uniquely expressed in control samples, 51 in stressed samples, and 74 shared between conditions, indicating stress-dependent circularization. Junction-read analysis highlighted a spectrum of circularization strength, ranging from highly abundant circRNAs with dominant junction reads to low-confidence candidates masked by linear transcript background. Genomic annotation showed that circRNAs primarily originated from exonic and intergenic regions, with a pronounced negative-strand bias; several genes generated multiple circRNA isoforms via alternative back-splicing. Functional enrichment of host genes suggested involvement in protein folding, nutrient reservoir activity, RNA degradation, and branched-chain amino acid catabolism, implicating roles in stress adaptation and metabolic regulation. Differential expression analysis identified seven circRNAs specifically induced under combined stress conditions. Network topology analysis pinpointed key miRNAs-including osa-miR414, osa-miR1439, and osa-miR2919-as candidate topological hubs within the predicted network. Their predicted target genes, such as those encoding stress-responsive transcription factors and signaling proteins, suggest potential roles in coordinating post-transcriptional responses to combined stress. Network topology analysis pinpointed key miRNAs-including osa-miR414, osa-miR1439, and osa-miR2919-as candidate topological hubs within the predicted network. Their predicted target genes, such as those encoding stress-responsive transcription factors and signaling proteins, suggest potential roles in coordinating post-transcriptional responses to combined stress. Overall, this study provides a comprehensive map of circRNAs in rice under combined heat and drought stress, suggests their potential as ceRNAs based on predictive analysis, and lays a foundation for future experimental validation of circRNA-mediated regulation.

Oryza↗
PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

ATF3↗
PubMed1977

Secondary-structure predictions of calcium-binding proteins.

The known tertiary structure of carp muscle parvalbumin is consistent with an "EF-hand" architecture (helix-loop-helix) for each calcium-ion binding site. Primary-sequence alignments have indicated four EF hands in rabbit skeletal muscle troponin C and in rabbit myosin alkali light chains. Five secondary-structure prediction methods, based on amino acid sequence only, have been fully computerized and used to calculate joint prediction histograms for several calcium-binding proteins. The joint histogram can suggest directly the extent and sequence of the helical- and loop-structural elements, as well as any secondary structural distortions or evolutionary developments. Since the histogram predicted well the length and sequence of secondary structural elements in carp muscle parvalbumin, it seemed reasonable to calculate the joint distribution for other proteins that might bind calcium through the EF-hand configuration. The histograms indicated the four EF-hand regions speculated fro rabbit skeletal muscle troponin C but suggested only three such hands in bovine cardiac muscle troponin C and with a distorted fourth hand. Considerable secondary structural distortion is postulated for the alkali light chains. Possible EF configurations consistent with the histogram results are speculated for Escherichia coli acyl-carrier protein and bovine prothrombin fragment 1, which have been shown to bind calcium. The secondary-structure-prediction algorithms appear to be a useful adjunct to sequence-alignment techniques, especially in cases where the primary sequence homology is weak or the evolutionary distance is large.

Amino Acid Sequence↗
PubMed1992

Substrate recognition by proteinases.

The molecular recognition of limited proteolytic site substrates by serine proteinases has been compared and contrasted to the recognition of serine proteinase inhibitors, utilising the coordinate sets contained in the Brookhaven Protein Databank. Most families of these inhibitors are known to possess a structurally conserved recognition motif at their reactive site-binding loops. Structural comparisons with trypsin limited proteolytic sites revealed that the in situ conformation of these substrates bears little resemblance to the inhibitor-binding loops. Assuming that both inhibitors and substrates bind to the proteinase in the same manner, segmental mobility would be required to permit substrates to adopt an 'inhibitor-like' binding conformation, which is presumed to be necessary for proteolysis. Modelling experiments have been conducted to attempt to introduce such a conformation into tryptic limited proteolytic segments of the native proteins, to test the ability of the limited proteolytic sites to alter their geometry. Further to this, the conformational parameters of accessibility, protrusion, mobility and secondary structure have been analysed and incorporated into a predictive algorithm to assign likely limited proteolytic sites within native protein structures.

Binding Sites↗
PubMed2026

Identifying fundamental gaps in functional metagenomics: a step towards unlocking microbiome research potential.

Incomplete functional annotation limits biological interpretation in microbiome studies and their translational potential. Poor annotation arises from multiple causes, with incomplete gene-protein-reaction mapping being one tractable yet under-examined contributor. We address this gap by developing a comprehensive hierarchical framework that systematically integrates gene families in UniRef, proteins in UniProt, and metabolic reactions in MetaCyc and BioCyc through UniProtKB accession, EC number, and Pfam-domain matching. Applied to a human gut metagenome dataset via HUMAnN3, our MetaCyc-based mapping recovers up to 2.3-fold more unique reaction identifiers than the default pipeline and increases reaction prevalence across samples from ≈32% to 52% core reactions, addressing the data sparsity that limits statistical and machine-learning applications in microbiome research. Biological plausibility for the tested functions was supported by positive and negative controls: gut-microbial hormone-metabolism reactions previously linked to this dataset were recovered, while vertebrate-specific hormone-metabolism reactions remained correctly undetected. These gains derive from systematic database integration alone, without predictive algorithms, indicating that a tractable, mapping-related component of functional dark matter and data sparsity in microbiome studies is directly addressable. Because Pfam- and BioCyc-derived mappings trade specificity for coverage, confidence in any individual reaction assignment depends on the supporting evidence tier and source database.

Humans↗
PubMed1992

Nuclear magnetic resonance studies and molecular dynamics simulations of the solution conformation of a 'designed', alpha-helical peptide.

The complete three-dimensional structure in methanol of an amphipathic alpha-helical peptide, that has been designed by taking into account the three-dimensional structures of small haemolytic peptides, secondary structure prediction algorithms and the well documented literature on alpha-helix stabilizing factors, has been elucidated by two-dimensional NMR spectroscopy. Initially various two-dimensional spectra (COSY, TOCSY, and NOESY) allowed the complete sequence specific assignment of all signals in the 1H spectrum. Consequently trial structures were generated which were then subjected to molecular dynamics simulations using 121 NOE-derived distances and 25 vicinal coupling constant values as structural restraints to give a final set of calculated structures. These structures are in complete agreement with the results of a circular dichroism study and reveal that the peptide adopted a highly ordered alpha-helical conformation. Details of the structure which throw light on future peptide/protein design are discussed.

Amino Acid Sequence↗