Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms

One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer’s (AD) and Parkinson’s Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions

The PRINTS database: a fine-grained protein sequence annotation and analysis resource—its status in 2012

The PRINTS database, now in its 21st year, houses a collection of diagnostic protein family ‘fingerprints’. Fingerprints are groups of conserved motifs, evident in multiple sequence alignments, whose unique inter-relationships provide distinctive signatures for particular protein families and structural/functional domains. As such, they may be used to assign uncharacterized sequences to known families, and hence to infer tentative functional, structural and/or evolutionary relationships. The February 2012 release (version 42.0) includes 2156 fingerprints, encoding 12 444 individual motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Here, we report the current status of the database, and introduce a number of recent developments that help both to render a variety of our annotation and analysis tools easier to use and to make them more widely available

Fast index based algorithms and software for matching position specific scoring matrices

BACKGROUND: In biological sequence analysis, position specific scoring matrices (PSSMs) are widely used to represent sequence motifs in nucleotide as well as amino acid sequences. Searching with PSSMs in complete genomes or large sequence databases is a common, but computationally expensive task. RESULTS: We present a new non-heuristic algorithm, called ESAsearch, to efficiently find matches of PSSMs in large databases. Our approach preprocesses the search space, e.g., a complete genome or a set of protein sequences, and builds an enhanced suffix array that is stored on file. This allows the searching of a database with a PSSM in sublinear expected time. Since ESAsearch benefits from small alphabets, we present a variant operating on sequences recoded according to a reduced alphabet. We also address the problem of non-comparable PSSM-scores by developing a method which allows the efficient computation of a matrix similarity threshold for a PSSM, given an E-value or a p-value. Our method is based on dynamic programming and, in contrast to other methods, it employs lazy evaluation of the dynamic programming matrix. We evaluated algorithm ESAsearch with nucleotide PSSMs and with amino acid PSSMs. Compared to the best previous methods, ESAsearch shows speedups of a factor between 17 and 275 for nucleotide PSSMs, and speedups up to factor 1.8 for amino acid PSSMs. Comparisons with the most widely used programs even show speedups by a factor of at least 3.8. Alphabet reduction yields an additional speedup factor of 2 on amino acid sequences compared to results achieved with the 20 symbol standard alphabet. The lazy evaluation method is also much faster than previous methods, with speedups of a factor between 3 and 330. CONCLUSION: Our analysis of ESAsearch reveals sublinear runtime in the expected case, and linear runtime in the worst case for sequences not shorter than | [Formula: see text] |(m )+ m - 1, where m is the length of the PSSM and [Formula: see text] a finite alphabet. In practice, ESAsearch shows superior performance over the most widely used programs, especially for DNA sequences. The new algorithm for accurate on-the-fly calculations of thresholds has the potential to replace formerly used approximation approaches. Beyond the algorithmic contributions, we provide a robust, well documented, and easy to use software package, implementing the ideas and algorithms presented in this manuscript

Using structural motif descriptors for sequence-based binding site prediction

All authors are with the Biotechnological Center, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany and -- Wan Kyu Kim is with the Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USABackground: Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites. -- Results: We generate descriptors for 740 classified types of protein-protein binding sites and for more than 3,000 protein-ligand binding sites. Cross validation reveals that two thirds of the PPI descriptors are sufficiently conserved and significant enough to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally identify the interface residues. Finally we test ligand-binding descriptors for the case of ATP. From sequences with Swiss-Prot annotation "ATP-binding", we achieve a recall of 25% with a precision of 89%, whereas Prosite's P-loop motif recognizes an equal amount of hits at the expense of a much higher number of false positives (precision: 57%). Our method yields 771 hits with a precision of 96% that were not previously picked up by any Prosite-pattern. -- Conclusion: The automatically generated descriptors are a useful complement to known Prosite/InterPro motifs. They serve to predict protein-protein as well as protein-ligand interactions along with their binding site residues for proteins where merely sequence information is available.Institute for Cellular and Molecular [email protected]

Evolutionary History of Tissue Kallikreins

The gene family of human kallikrein-related peptidases (KLKs) encodes proteins with diverse and pleiotropic functions in normal physiology as well as in disease states. Currently, the most widely known KLK is KLK3 or prostate-specific antigen (PSA) that has applications in clinical diagnosis and monitoring of prostate cancer. The KLK gene family encompasses the largest contiguous cluster of serine proteases in humans which is not interrupted by non-KLK genes. This exceptional and unique characteristic of KLKs makes them ideal for evolutionary studies aiming to infer the direction and timing of gene duplication events. Previous studies on the evolution of KLKs were restricted to mammals and the emergence of KLKs was suggested about 150 million years ago (mya). In order to elucidate the evolutionary history of KLKs, we performed comprehensive phylogenetic analyses of KLK homologous proteins in multiple genomes including those that have been completed recently. Interestingly, we were able to identify novel reptilian, avian and amphibian KLK members which allowed us to trace the emergence of KLKs 330 mya. We suggest that a series of duplication and mutation events gave rise to the KLK gene family. The prominent feature of the KLK family is that it consists of tandemly and uninterruptedly arrayed genes in all species under investigation. The chromosomal co-localization in a single cluster distinguishes KLKs from trypsin and other trypsin-like proteases which are spread in different genetic loci. All the defining features of the KLKs were further found to be conserved in the novel KLK protein sequences. The study of this unique family will further assist in selecting new model organisms for functional studies of proteolytic pathways involving KLKs

Outcomes following supervised exercise and home-based exercise for patients with intermittent claudication

Introduction Intermittent claudication (IC) is the most common symptom of peripheral arterial disease (PAD) which presents as a consequence of muscle ischaemia resulting from the atherosclerotic obstruction to arterial flow. High-quality evidence (Lane et al., 2017) shows that exercise programmes provide important benefits compared with usual care in improving pain-free and maximum walking distance in people with IC, but do not improve ankle-brachial pressure index (ABPI). Methods Retrospective data were analysed to examine walking and ABPI outcomes for participants who completed a 12-week course of supervised or home-based exercise. All participants had a history of IC. Results 46 participants (mean age 69±11 years; 76% male; 29% current smokers) referred for exercise were assessed, completed a 12-week course of exercise (home-based or supervised) and subsequently attended for re-assessment. Claudication onset distance (COD) increased by 363% (mean improvement 344.7 ± 265.1m; p < .001) and peak walking distance (PWD) by 324.4% in the supervised exercise group; COD increased by 30.6% (mean improvement 32.8 ± 57.2 m; p = 0.026) and PWD by 31.5% in the home-based exercise group. Resting ABPI for the total cohort significantly improved from 0.82 ± 0.25 at A1 to 0.88 ± 0.25 at A2 (p = 0.027). Discussion A 12-week course of supervised exercise results in significantly greater walking distance outcomes (COD and PWD) than unmonitored home-based exercise. In contrast with previous findings (Lane et al. 2017), this retrospective study demonstrated a significant improvement in resting ABPI with both supervised exercise as well as home-based exercise. Conclusion A 12-week programme of exercise favourably influenced walking and ABPI outcomes for patients with IC. Both home-based exercise and supervised individualised exercise increased walking distances, but the magnitude of the improvement in walking outcomes was greater in individuals who attended supervised exercise therapy

PRINTS-S: the database formerly known as PRINTS

The PRINTS database houses a collection of protein family fingerprints. These are groups of motifs that together are diagnostically more potent than single motifs by virtue of the biological context afforded by matching motif neighbours. Around 1200 fingerprints have now been created and stored in the database. The September 1999 release (version 24.0) encodes ~7200 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. In addition to its continued steady growth, we report here several major changes to the resource, including the design of an automated strategy for database maintenance, and implementation of an object-relational schema for more efficient data management. The database is accessible for BLAST, fingerprint and text searches at http://www.bioinf.man.ac.uk/dbbrowser/PRINTS

PRINTS prepares for the new millennium

PRINTS is a diagnostic collection of protein fingerprints. Fingerprints exploit groups of motifs to build characteristic family signatures, offering improved diagnostic reliability over single-motif approaches by virtue of the mutual context provided by motif neighbours. Around 1000 fingerprints have now been created and stored in PRINTS. The September 1998 release (version 20.0), encodes approximately 5700 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. The database is accessible via the DbBrowser Web Server at http://www.biochem.ucl.ac.uk/bsm/dbbrowser /. In addition to supporting its continued growth, recent enhancements to the resource include a BLAST server, and more efficient fingerprint search software, with improved statistics for estimating the reliability of retrieved matches. Current efforts are focused on the design of more automated methods for database maintenance; implementation of an object-relational schema for efficient data management; and integration with PROSITE, profiles, Pfam and ProDom, as part of the international InterPro project, which aims to unify protein pattern databases and offer improved tools for genome analysis