Lipoprotein(a) [Lp(a)] is an independent risk factor for atherosclerotic cardiovascular disease. Although Lp(a) levels are generally stable, the extent of intra-individual variation and the need for repeat Lp(a) testing is unclear. This retrospective study analysed 250 patients from a tertiary care lipid clinic with ≥2 Lp(a) measurements over a mean of 17.1 ± 15.5 months. Baseline levels were positively skewed (median of 56.0 nmol/L; interquartile range 21.0-154.3 nmol/L). Intra-individual coefficients of variation (CV) were 19.0% (mean-based) and 33.6% (log-transformed), exceeding the European Federation of Clinical Chemistry and Laboratory Medicine database CV (10.2%; 4.3–26.7%). Cardiovascular risk reclassification occurred for 12.4% using the National Lipid Association thresholds (75 and 125 nmol/L) and 6.8% using the European Society of Cardiology threshold (105 nmol/L). Variability was not associated with time between measurements, medications, or biochemical parameters on multivariable analysis. Hence, repeat Lp(a) testing is generally unnecessary but could be considered in those near risk thresholds, or those being evaluated for Lp(a) lowering therapies.

Small-interfering RNA (siRNA)-based therapies, such as inclisiran, offer a novel approach to reducing low-density lipoprotein-cholesterol (LDL-C) and preventing cardiovascular disease (CVD). Inclisiran inhibits proprotein convertase subtilisin/kexin type 9 (PCSK9) synthesis, enhancing LDL-receptor recycling and LDL-particle clearance. Although clinical trials have established its efficacy with LDL-C reductions of 44-52%, real-world studies have reported variable reductions over shorter follow-up periods. This study aimed to assess the long-term efficacy and safety of inclisiran in a diverse real-world cohort. A total of 238 patients initiating inclisiran between January 2022 and January 2024 at a tertiary lipid service were included. Data on lipid profiles, comorbidities, and medication history were collected from electronic healthcare records. LDL-C reductions were analysed at each dose using a Bayesian hierarchical model, and subgroup analyses explored the influence of familial hypercholesterolaemia (FH) and baseline lipid-lowering therapy. Inclisiran therapy resulted in a mean LDL-C reduction of 48.4% following the first dose, sustained over 27 months. Patients receiving three or more lipid-lowering therapies at baseline achieved greater LDL-C reductions compared with others (67.4% after first dose vs. 47.6%). No discernible differences in efficacy were observed between patients with and without FH. Inclisiran was well tolerated, with only six patients discontinuing therapy due to adverse events or preference. Approximately 35% of patients met the European Society of Cardiology LDL-C target of <1.4 mmol/L after the first dose, declining to 28% after the fourth dose. These real-world findings demonstrate that inclisiran is a well-tolerated and effective lipid-lowering therapy, achieving reductions comparable with clinical trial results. Greater reductions in patients on multiple baseline therapies suggest the importance of comprehensive lipid management. While achieving stringent LDL-C targets remains challenging, inclisiran's practical benefits, including infrequent dosing and good tolerability, underscore its potential to improve CVD outcomes in diverse populations.

BACKGROUND:Lipoprotein(a) [Lp(a)] is an independent risk factor for atherosclerotic cardiovascular disease. Although Lp(a) levels are generally stable, the extent of intraindividual variation and the need for repeat Lp(a) testing remain unclear. OBJECTIVE:To evaluate the intraindividual variation in Lp(a) levels assess the clinical impact of repeat testing on cardiovascular risk classification. METHODS:This retrospective study analyzed 250 patients from a tertiary care lipid clinic with ≥2 Lp(a) measurements over a mean of 17.1 ± 15.5 months. RESULTS:Baseline levels were positively skewed (median of 56.0 nmol/L; interquartile range 21.0-154.3 nmol/L). Intraindividual coefficients of variation (CV) were 19.0% (mean-based) and 33.6% (log-transformed), exceeding the European Federation of Clinical Chemistry and Laboratory Medicine database CV (10.2%; 4.3%-26.7%). Cardiovascular risk reclassification occurred for 12.4% using the National Lipid Association thresholds (75 and 125 nmol/L) and 6.8% using the European Society of Cardiology threshold (105 nmol/L). Variability was not associated with time between measurements, medications, or biochemical parameters on multivariable analysis. CONCLUSION:Hence, repeat Lp(a) testing is generally unnecessary but could be considered in those near risk thresholds or those being evaluated for Lp(a)-lowering therapies.

UniProt is a central repository of protein sequences and annotations, with entries being updated several times a year as new sequencing evidence is collected. By contrast, protein structure resources often evolve at a different pace. The AlphaFold database remained unchanged for four years, until September 2025, during which time nearly 3% of the associated sequences underwent revisions in UniProt. In a range of bioinformatics tasks, protein structure data is paired with sequence annotations from UniProt. Mapping annotations to outdated structure files can lead to errors in downstream analysis. While this concern has been addressed for experimental structures, efforts for the modeled structures are lacking. 3DSeqCheck is a lightweight web tool that enables quick comparison of the sequence of modeled and experimental structures to the latest UniProt entries. 3DSeqCheck provides an interactive visual panel of the alignment and the comparison of the residue numbering and can be accessed freely at: https://missense3d.bc.ic.ac.uk/3dseqcheck and https://github.ic.ac.uk/ImperialCollegeLondon/check3Dseq.

The AlphaFold database, released in 2022, modeled UniProt sequences from April 2021 and now provides 200 million predicted protein structures. Of the 20,504 full-length predicted human structures, 631 entries conflict with the June 2025 UniProt release. Similar conflicts across species highlight how bioinformatics resources can rapidly age.

Deep learning methods have revolutionised our ability to predict protein structures, allowing us a glimpse into the entire protein universe. As a result, our understanding of how protein structure drives function is now lagging behind our ability to determine and predict protein structure. Here, we describe how topology, the branch of mathematics concerned with qualitative properties of spatial structures, provides a lens through which we can identify fundamental organising features across the known protein universe. We identify topological determinants that capture global features of the protein universe, such as domain architecture and binding sites. Additionally, our analysis identifies highly specific properties, so-called topological generators, that can be used to provide deeper insights into protein structure-function and evolutionary relationships. We present a practical methodology for mapping the topology of the known protein universe at scale. We then use our approach to determine structural, functional and disease consequences of mutations. Our approach reveals and helps to explain differences in properties of proteins in mesophiles and thermophiles, and the likely structural and functional consequences of polymorphisms in a protein. For eukaryotes we find striking differences between protein topologies in multi-cellular and single-celled organisms.

OBJECTIVE:Heterozygous germline loss-of-function variants in AIP are associated with young-onset growth hormone and/or prolactin-secreting pituitary tumours. However, the pathogenic role of the c.911G > A; p.(Arg304Gln) (R304Q) AIP variant has been controversial. Recent data from public exome/genome databases show this variant is not infrequent. The objective of this work was to reassess the pathogenicity of R304Q based on clinical, genomic, and functional assay data. DESIGN:Data were collected on published R304Q pituitary neuroendocrine tumour cases and from International Familial Isolated Pituitary Adenoma Consortium R304Q cases (n = 38, R304Q cohort). Clinical features, population cohort frequency, computational analyses, prediction models, presence of loss-of-heterozygosity, and in vitro/in vivo functional studies were assessed and compared with data from pathogenic/likely pathogenic AIP variant patients (AIPmut cohort, n = 184). RESULTS:Of 38 R304Q patients, 61% (23/38) had growth hormone excess, in contrast to 80% of AIPmut cohort (147/184, P < .001). R304Q cohort was older at disease onset and diagnosis than the AIPmut cohort (median [quartiles] onset: 25 y [16-35] vs 16 y [14-23], P < .001; median [quartiles] diagnosis: 36 y [24-44] vs 21 y [15-29], P < .001). R304Q is present in gnomADv2.1 (0.31%) and UK Biobank (0.16%), including three persons with homozygous R304Q. No loss-of-heterozygosity was detected in four R304Q pituitary neuroendocrine tumour samples. In silico predictions and experimental data were conflicting. CONCLUSIONS:Evidence suggests that R304Q is not pathogenic for pituitary neuroendocrine tumour. We recommend changing this variant classification to likely benign and do not recommend pre-symptomatic genetic testing of family members or follow-up of already identified unaffected individuals with the R304Q variant.

Only a fraction of the >11 million missense variants identified in humans has a known clinical significance. Post-translational modifications (PTMs), such as phosphorylation, glycosylation and ubiquitination, are key regulators of protein function and structure. PTMs depend on correct protein folding and the recognition and binding of enzymes to specific amino acid motifs near modification sites. AlphaFold models provide an unprecedented opportunity to explore variants on 3D structures, enabling systematic identification of amino acid substitutions that could affect PTMs and should be further investigated experimentally. We present Missense3D-PTMdb, a "one-stop-shop" interactive web tool that provides a user-friendly sequence-structure mapping of 20,235 human proteins, 11,5 million naturally occurring human missense variants, >60 PTM types and 203,775 PTM residues and their neighbours in sequence and 3D structure space using AlphaFold models of the human proteome. The resource also supports visualisation of novel variants not in the database. Missense3D-PTMdb is freely available at https://missense3d.bc.ic.ac.uk/ptmdb.

Only a fraction of the >11 million missense variants identified in the human population has a known damaging or tolerated clinical impact. Post-translational modifications (PTMs), such as phosphorylation, glycosylation and ubiquitination, are key regulators of protein function and structure, and are critical for protein localisation, stability and interactions with other molecules. The ability of a protein to undergo PTMs, is subject to a correctly folded protein structure, and the recognition and binding of enzymes to specific amino acid motifs in close proximity to residues that undergo PTMs [PTM residue]. AlphaFold models provide an unprecedented opportunity to perform sequence-structure mapping of variants, which are in close linear or spatial proximity to a PTM site and should have their impact on protein function experimentally investigated. We present Missense3D-PTMdb, a “one-stop-shop” interactive web tool that provides a user-friendly sequence-structure mapping of 20,235 human proteins to 11,544,303 naturally occurring human missense variants, 203,775 PTM sites and their neighbours in sequence and 3D structure space using AlphaFold generated 3D models of the human proteome. Additionally, the sequence-structure mapping tool allows visualization and exploration of any human variant not currently stored in the database. Missense3D-PTMDb is freely available at . ### Competing Interest Statement The authors have declared no competing interest. Medical Research Council, https://ror.org/03x94j517, MR/Y031091/1

Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of a known structure as they will adopt a similar conformation, and the known structure can be used as the template for modelling the query sequence. Recently several approaches, most notably AlphaFold, have employed enhanced machine learning and have yielded accurate models irrespective of whether there is an identifiable template. Here we report Phyre2.2 which incorporates several enhancements to our widely-used template modelling portal Phyre2. The main development is facilitating a user to submit their sequence and then Phyre2.2 identifies the most suitable AlphaFold model to be used as a template. In Phyre2.2 the user searches a template library of known structures. We have now included in our library a representative structure for every protein sequence in the protein databank (PDB). In addition, there are representatives for an apo and a holo structure if they are in the PDB. The ranking of hits has been modified to highlight to the user if there are different domains spanning the sequence. Phyre2.2 continues to support batch processing where a user can submit up to 100 sequences facilitating processing of proteomes. Phyre2.2 is freely available to all users, including commercial users, at https://www.sbg.bio.ic.ac.uk/phyre2/. (c) 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecom-mons.org/licenses/by/4.0/).

The AlphaFold database provides 200M protein structures predicted by AlphaFold2 in 2022 from sequences in UniProt. However, of the 20,504 full-length human structures in the AlphaFold database, 575 entries conflict with UniProt (version 2024_05); and there is a similar discrepancy for other species. This highlights how bioinformatics resources as exemplified by the AlphaFold database can rapidly age. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust, 218242/Z/19/z Biotechnology and Biological Sciences Research Council, BB/T010487/, BB/V018558/1 Medical Research Council, MR/Y031091/1

Variant effect predictors assess if a substitution is pathogenic or benign. Most predictors, including those that are structure-based, are designed for globular proteins in aqueous environments and do not consider that the variant residue is located within the membrane. We report Missense3D-TM that provides a structure-based assessment of the impact of a missense variant located within a membrane. On a data -set of 2,078 pathogenic and 1,060 benign variants, spanning 711 proteins from 706 structures, Missense3D-TM achieved an accuracy of 66%, Mathews correlation coefficient of 0.37, sensitivity of 58% and specificity of 81%. Missense3D-TM performed similarly to mCSM-membrane: accuracy 66% vs 61% (p = 0.02) on an unbalanced test set and 70% vs 67% (p = 0.20) on a balanced test set. The Missense3D-TM website provides an analysis of the structural effects of the variant along with its pre-dicted position within the membrane. The web server is available at http://missense3d.bc.ic.ac.uk/. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecom-mons.org/licenses/by/4.0/).