| 51. |
- Emanuelsson, Olof, et al.
(författare)
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In silico prediction of the peroxisomal proteome in fungi, plants and animals.
- 2003
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Ingår i: Journal of Molecular Biology. - 0022-2836 .- 1089-8638. ; 330:2, s. 443-456
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Tidskriftsartikel (refereegranskat)abstract
- In an attempt to improve our abilities to predict peroxisomal proteins, we have combined machine-learning techniques for analyzing peroxisomal targeting signals (PTS1) with domain-based cross-species comparisons between eight eukaryotic genomes. Our results indicate that this combined approach has a significantly higher specificity than earlier attempts to predict peroxisomal localization, without a loss in sensitivity. This allowed us to predict 430 peroxisomal proteins that almost completely lack a localization annotation. These proteins can be grouped into 29 families covering most of the known steps in all known peroxisomal pathways. In general, plants have the highest number of predicted peroxisomal proteins, and fungi the smallest number.
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| 52. |
- Eriksson, Olivia, PhD, 1971-, et al.
(författare)
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Side Chain-Positioning as an Integer Programming Problem.
- 2001
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Nature. ; , s. 128-141
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Konferensbidrag (refereegranskat)abstract
- An important aspect of homology modeling and protein design algorithms is the correct positioning of protein side chains on a fixed backbone. Homology modeling methods are necessary to complement large scale structural genomics projects. Recently it has been shown that in automatic protein design it is of the uttermost importance to find the global solution to the side chain positioning problem [1]. If a suboptimal solution is found the difference in free energy between different sequences will be smaller than the error of the side chain positioning. Several different algorithms have been developed to solve this problem. The most successful methods use a discrete representation of the conformational space. Today, the best methods to solve this problem, are based on the dead end elimination theorem. Here we introduce an alternative method. The problem is formulated as a linear integer program. This programming problem can then be solved by efficient polynomial time methods, using linear programming relaxation. If the solution to the relaxed problem is integral it corresponds to the global minimum energy conformation (GMEC). In our experimental results, the solution to the relaxed problem has always been integral.
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| 53. |
- Eriksson, Olivia, 1971-
(författare)
-
Simplicity within Complexity : Understanding dynamics of cellular networks by model reduction
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cellular networks composed of interactions between genes, proteins and metabolites, determines the behavioural repertoire of the cell. Recent developments in high-throughput experimental techniques and computational methods allow static descriptions of these networks on a genome scale. There are also several dynamical mathematical models characterizing small subnetworks of the cell such as a signaling cascade or cell division. These networks exhibit a considerable complexity, and mathematical analysis are therefore essential in order to uncover the underlying dynamical core driving the systems. A core description can reveal the relative functional contributions of the various molecular interactions and goes to the heart of what kind of computations biological circuits perform. Partially successful methodologies toward this end includes bifurcation analysis, which only considers a small number of dimensions, and large-scale computer simulations. In this thesis we explore a third route utilizing the inherent biological structure and dynamics of the network as a tool for model simplification. Using the well studied cell cycle, as a model system, we observe that the this network can be divided into dynamical modules displaying a switch-like behaviour. This allows a transformation into a piecewise linear system with delay, the subsequent use of tools from linear systems theory and finally a core dynamical description. Analytical expressions capturing important cell cycle features such as cell mass, as well as necessary constraints for cell cycle oscillations, are thereby retrieved. Finally we use the dynamical core together with large-scale simulations in order to study the balance between robustness and sensitivity. It appears that biological features such as switches, modularity and robustness provide a means to reformulate intractable mathematical problems into solvable ones, as biology appears to suggest a path of simplicity within the realm of mathematical complexity.
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| 54. |
- Ernits, Karin, et al.
(författare)
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The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin-antitoxin and related phage defense systems
- 2023
-
Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - 1091-6490 .- 0027-8424. ; 120:33, s. 1-12
-
Tidskriftsartikel (refereegranskat)abstract
- Toxin-antitoxin (TA) systems are a large group of small genetic modules found in prokaryotes and their mobile genetic elements. Type II TAs are encoded as bicistronic (two-gene) operons that encode two proteins: a toxin and a neutralizing antitoxin. Using our tool NetFlax (standing for Network-FlaGs for toxins and antitoxins), we have performed a large-scale bioinformatic analysis of proteinaceous TAs, revealing interconnected clusters constituting a core network of TA-like gene pairs. To understand the structural basis of toxin neutralization by antitoxins, we have predicted the structures of 3,419 complexes with AlphaFold2. Together with mutagenesis and functional assays, our structural predictions provide insights into the neutralizing mechanism of the hyperpromiscuous Panacea antitoxin domain. In antitoxins composed of standalone Panacea, the domain mediates direct toxin neutralization, while in multidomain antitoxins the neutralization is mediated by other domains, such as PAD1, Phd-C, and ZFD. We hypothesize that Panacea acts as a sensor that regulates TA activation. We have experimentally validated 16 NetFlax TA systems and used domain annotations and metabolic labeling assays to predict their potential mechanisms of toxicity (such as membrane disruption, and inhibition of cell division or protein synthesis) as well as biological functions (such as antiphage defense). We have validated the antiphage activity of a RosmerTA system encoded by Gordonia phage Kita, and used fluorescence microscopy to confirm its predicted membrane-depolarizing activity. The interactive version of the NetFlax TA network that includes structural predictions can be accessed at http://netflax.webflags.se/.
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| 55. |
- Govindarajan, Sudha, et al.
(författare)
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The evolutionary history of topological variations in the CPA/AT superfamily
- 2024
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- CPA/AT transporters consist of two structurally and evolutionarily related inverted repeat units, each of them with one core and one scaffold subdomain. During evolution, these families have undergone substantial changes in structure, topology and function. Central to the function of the transporters is the existence of two noncanonical helices that are involved in the transport process. In different families, two different types of these helices have been identified, reentrant and broken. Here, we use an integrated topology annotation method to identify novel topologies in the families. It combines topology prediction, similarity to families with known structure, and the difference in positively charged residues present in inside and outside loops in alternative topological models. We identified families with diverse topologies containing broken or reentrant helix. We classified all families based on 3 distinct evolutionary groups that each share a structurally similar C-terminal repeat unit newly termed as “Fold-types”. Using the evolutionary relationship between families we propose topological transitions including, a transition between broken and reentrant helices, complete change of orientation, changes in the number of scaffold helices and even in some rare cases, losses of core helices. The evolutionary history of the repeat units shows gene duplication and repeat shuffling events to result in these extensive topology variations. The novel structure-based classification, together with supporting structural models and other information, is presented in a searchable database, CPAfold (cpafold.bioinfo.se). Our comprehensive study of topology variations within the CPA superfamily provides better insight about their structure and evolution.
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| 56. |
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| 57. |
- Granseth, Erik, 1978-
(författare)
-
Structure, prediction, evolution and genome wide studies of membrane proteins
- 2007
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- α-helical membrane proteins constitute 20-30% of all proteins in a cell and are involved in many essential cellular functions. The structure is only known for a few hundred of them, which makes structural models important. The most common structural model of a membrane protein is the topology which is a two-dimensional representation of the structure. This thesis is focused on three different aspects of membrane protein structure: improving structural predictions of membrane proteins, improving the level of detail of structural models and the concept of dual topology. It is possible to improve topology models of membrane proteins by including experimental information in computer predictions. This was first performed in Escherichia coli and, by using homology, it was possible to extend the results to 225 prokaryotic organisms. The improved models covered ~80% of the membrane proteins in E. coli and ~30% of other prokaryotic organisms. However, the traditional topology concept is sometimes too simple for complex membrane protein structures, which create a need for more detailed structural models. We created two new machine learning methods, one that predicts more structural features of membrane proteins and one that predicts the distance to the membrane centre for the amino acids. These methods improve the level of detail of the structural models. The final topic of this thesis is dual topology and membrane protein evolution. We have studied a class of membrane proteins that are suggested to insert either way into the membrane, i.e. have a dual topology. These protein families might explain the frequent occurrence of internal symmetry in membrane protein structures.
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| 58. |
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| 59. |
- Grapotte, M, et al.
(författare)
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Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
- 2021
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1, s. 3297-
-
Tidskriftsartikel (refereegranskat)abstract
- Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
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| 60. |
- Hargbo, Jeanette, et al.
(författare)
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Hidden Markov Models That Use Predicted Secondary Structures For Fold Recognition
- 1999
-
Ingår i: Proteins. - 0887-3585 .- 1097-0134. ; 36:1, s. 68-76
-
Tidskriftsartikel (refereegranskat)abstract
- There are many proteins that share the same fold but have no clear sequence similarity. To predict the structure of these proteins, so called protein fold recognition methods have been developed. During the last few years, improvements of protein fold recognition methods have been achieved through the use of predicted secondary structures (Rice and Eisenberg, J Mol Biol 1997;267:1026-1038), as well as by using multiple sequence alignments in the form of hidden Markov models (HMM) (Karplus et al., Proteins Suppl 1997;1:134-139). To test the performance of different fold recognition methods, we have developed a rigorous benchmark where representatives for all proteins of known structure are matched against each other. Using this benchmark, we have compared the performance of automatically-created hidden Markov models with standard-sequence-search methods. Further, we combine the use of predicted secondary structures and multiple sequence alignments into a combined method that performs better than methods that do not use this combination of information. Using only single sequences, the correct fold of a protein was detected for 10% of the test cases in our benchmark. Including multiple sequence information increased this number to 16%, and when predicted secondary structure information was included as well, the fold was correctly identified in 20% of the cases. Moreover, if the correct secondary structure was used, 27% of the proteins could be correctly matched to a fold. For comparison, blast2, fasta, and ssearch identifies the fold correctly in 13-17% of the cases. Thus, standard pairwise sequence search methods perform almost as well as hidden Markov models in our benchmark. This is probably because the automatically-created multiple sequence alignments used in this study do not contain enough diversity and because the current generation of hidden Markov models do not perform very well when built from a few sequences. Proteins 1999;36:68-76
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