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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  Human Genetics Vol. 141, No. 10 ( 2022-10), p. 1629-1647
    In: Human Genetics, Springer Science and Business Media LLC, Vol. 141, No. 10 ( 2022-10), p. 1629-1647
    Abstract: The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient—MCC—for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.
    Type of Medium: Online Resource
    ISSN: 0340-6717 , 1432-1203
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
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  • 2
    In: Protein Science, Wiley, Vol. 32, No. 1 ( 2023-01)
    Abstract: The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha‐helical and beta‐barrel transmembrane segments; signal‐peptides; variant effect) in seconds. The structure prediction provided by LambdaPP —leveraging ColabFold and computed in minutes —is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5 . Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org , the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2 . The frontend of LambdaPP can be found on GitHub ( github.com/sacdallago/embed.predictprotein.org ), and can be freely used and distributed under the academic free use license (AFL‐2). For high‐throughput applications, all methods can be executed locally via the bio‐embeddings ( bioembeddings.com ) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings , which also includes the backend of LambdaPP.
    Type of Medium: Online Resource
    ISSN: 0961-8368 , 1469-896X
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
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  • 3
    In: Alzheimer's & Dementia, Wiley, Vol. 17, No. S4 ( 2021-12)
    Abstract: The NIA‐AA proposed ATN (Amyloid/Tau/Neurodegeneration) as a classification system for AD pathology. The Amyloid Cascade Hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A‐T‐N‐→A+T‐N‐→A+T+N‐→A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (Amyloid‐conversion first, Tau‐conversion second, N‐conversion last; therefore ‘ATN’) and alternative progressions (ANT, TAN, TNA, NAT, NTA) using Voxel‐based Morphometry (VBM) of brain anatomy in a large MRI sample. Method We used the DELCODE cohort of 437 subjects (49% female) which underwent lumbar puncture, MRI scanning and neuropsychological assessment. ATN classification was performed using (A+/‐) CSF‐Abeta42over40, (T+/‐) CSF‐phospho‐Tau, and (N+/‐) adjusted hippocampal volume. We compared voxel‐based model evidence for monotonic decline of gray matter volume across various sequences over ATN groups accounting for age, sex, education, TIV and WMH. The evidence of each progression was assessed using the Bayesian Information Criterion on voxel‐ and ROI‐level. First, face validity of the ACH transition trajectory A‐T‐N‐→A+T‐N‐→A+T+N‐→A+T+N+ for VBM was compared against 23 biologically less plausible (permuted) sequences among AD‐continuum ATN groups. Then we evaluated the evidence for 6 brain volume progressions from A‐T‐N‐ towards A+T+N+ (ATN, ANT, TAN, TNA, NAT, NTA) including also non‐AD continuum ATN groups. Result The ACH‐based progression A‐T‐N‐→A+T‐N‐→A+T+N‐→A+T+N+ is in line with cognitive decline and clinical diagnosis (Figure 1 & 2). It also has highest evidence in 9% of the gray matter voxels (especially MTL; Figure 3 & 4). Many (especially cortical) regions were compatible with alternative non‐monotonic volume progressions (‘AP 1’: 16%, ‘AP 2’: 14%; see Figure 3) over ACH progression sequence, compatible with early amyloid‐related tissue expansion or sampling effects due to brain‐reserve (Figure 5). Volume decline in 65% of voxels was more compatible with ATN/ANT progression (A flips first) when compared to alternative sequences (TAN, TNA, NAT, NTA). Conclusion Early Amyloid status conversion (before Tau and Neurodegeneration) is compatible with brain volume loss observed during AD progression. The ATN classification and the ACH are compatible with monotonic progress of MTL atrophy.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
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  • 4
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 3 ( 2021-05-20)
    Abstract: Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein’s functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 5
    In: Alzheimer's Research & Therapy, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2023-03-13)
    Abstract: The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A−T−N−➔A+T−N−➔A+T+N−➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort. Methods We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/−), CSF phospho-tau (T+/−), and adjusted hippocampal volume or CSF total-tau (N+/−). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A−T−N−➔A+T−N−➔A+T+N−➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A−T−N− towards A+T+N+ including also non-AD continuum ATN groups. Results The ACH-based progression A−T−N−➔A+T−N−➔A+T+N−➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead. Conclusion Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy. Trial registration DRKS00007966, 04/05/2015, retrospectively registered.
    Type of Medium: Online Resource
    ISSN: 1758-9193
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2506521-X
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  • 6
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 51, No. W1 ( 2023-07-05), p. W62-W69
    Abstract: Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: https://caid.idpcentral.org.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1472175-2
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  • 7
    In: Journal of Molecular Biology, Elsevier BV, Vol. 432, No. 7 ( 2020-03), p. 2428-2443
    Type of Medium: Online Resource
    ISSN: 0022-2836
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 1355192-9
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  • 8
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. W1 ( 2021-07-02), p. W535-W540
    Abstract: Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 9
    In: Alzheimer's & Dementia, Wiley, Vol. 16, No. S5 ( 2020-12)
    Abstract: Subjective cognitive decline (SCD) has been proposed as an early symptomatic representation of Alzheimer’s disease (AD). Per definition SCD implies patient’s memory being subjectively impaired while broad neuropsychological indicators are still in normal range (Fig.1A). SCD might show early signs of atrophy in networks typically affected during AD disease progression. Among individuals with SCD, the prevalence of older adults who are on a declining AD trajectory are more frequent than in cognitively normal (CN) elderly. The key distinction between SCD and MCI would be a higher offset in memory ability in SCD and a larger proportion of individuals who have progressed further along the AD spectrum. This predicts that brain volume variability would be higher in SCD than in CN and non‐complaining older adults. Methods We used sMRI at 3T to quantify anatomical differences using VBM in the DELCODE cohort in a subsample of 755 nondemented elderly (including 221 CN, 376 SCD & 158 MCI). We characterise memory ability using a composite score based on factor modelling (Wolfsgruber et al., under review) of neuropsychological tests. We explore group differences and the association of memory to local brain morphometry using GLM and SPM. We further analyze variability of local volumes across groups using voxel‐based Brown‐Forsythe‐Tests. Results While there were significant volumetric differences of MCI but not in SCD as compared to healthy controls (Fig1 C left & right panel), volumetric variability (across subjects) was higher within the SCD (and MCI) as compared to CN in hippocampus (Fig. 2A). Individual differences in memory‐ability were positively associated with posterior hippocampal volume in SCD, but showed no association with brain volume in CN (Fig. 2B‐C). In MCI, variability in memory‐ability was also related to posterior hippocampal volume and in addition also to the volume of the anterior hippocampus and entorhinal cortex. Conclusions In terms of morphometric variability and the association between morphometric measures and memory ability, the SCD group shows partial overlap with MCI and is distinct to cognitively normal, non‐complaining older adults. The posterior hippocampal region could be an area that explains interindividual memory variability in early stages of the Alzheimer’s disease spectrum.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2201940-6
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  • 10
    In: Bioinformatics, Oxford University Press (OUP), Vol. 39, No. 1 ( 2023-01-01)
    Abstract: CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues missed by state-of-the-art hidden Markov model (HMM)-based approaches. The method developed (CATHe) combines a neural network with sequence representations obtained from protein language models. It was assessed using a dataset of remote homologues having less than 20% sequence identity to any domain in the training set. Results The CATHe models trained on 1773 largest and 50 largest CATH superfamilies had an accuracy of 85.6 ± 0.4% and 98.2 ± 0.3%, respectively. As a further test of the power of CATHe to detect more remote homologues missed by HMMs derived from CATH domains, we used a dataset consisting of protein domains that had annotations in Pfam, but not in CATH. By using highly reliable CATHe predictions (expected error rate & lt;0.5%), we were able to provide CATH annotations for 4.62 million Pfam domains. For a subset of these domains from Homo sapiens, we structurally validated 90.86% of the predictions by comparing their corresponding AlphaFold2 structures with structures from the CATH superfamilies to which they were assigned. Availability and implementation The code for the developed models is available on https://github.com/vam-sin/CATHe, and the datasets developed in this study can be accessed on https://zenodo.org/record/6327572. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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