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  • 1.
    Aurell, Erik
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ekeberg, Magnus
    KTH.
    Inverse Ising Inference Using All the Data2012In: Physical Review Letters, ISSN 0031-9007, E-ISSN 1079-7114, Vol. 108, no 9, p. 090201-Article in journal (Refereed)
    Abstract [en]

    We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of l(1) regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.

  • 2.
    Ekeberg, Magnus
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hartonen, Tuomo
    Aurell, Erik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Aalto University, Finland.
    Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences2014In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 276, p. 341-356Article in journal (Refereed)
    Abstract [en]

    Direct-coupling analysis is a group of methods to harvest information about coevolving residues in a protein family by learning a generative model in an exponential family from data. In protein families of realistic size, this learning can only be done approximately, and there is a trade-off between inference precision and computational speed. We here show that an earlier introduced l(2)-regularized pseudolikelihood maximization method called plmDCA can be modified as to be easily parallelizable, as well as inherently faster on a single processor, at negligible difference in accuracy. We test the new incarnation of the method on 143 protein family/structure-pairs from the Protein Families database (PFAM), one of the larger tests of this class of algorithms to date.

  • 3.
    Ekeberg, Magnus
    et al.
    KTH, School of Computer Science and Communication (CSC).
    Lövkvist, Cecilia
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lan, Y.
    Weigt, M.
    Aurell, Erik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models2013In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 87, no 1, p. 012707-Article in journal (Refereed)
    Abstract [en]

    Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional (3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction but can also be network-propagated via intermediate sites; observed correlation is not enough to guarantee proximity. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems. In the context of protein sequences, the approach has been referred to as direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at http://plmdca.csc.kth.se/.

  • 4. Michel, Mirco
    et al.
    Skwark, Marcin J.
    Hurtado, David Menendez
    Ekeberg, Magnus
    KTH, School of Computer Science and Communication (CSC).
    Elofsson, Arne
    Predicting accurate contacts in thousands of Pfam domain families using PconsC32017In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 18, p. 2859-2866Article in journal (Refereed)
    Abstract [en]

    Motivation: A few years ago it was shown that by using a maximum entropy approach to describe couplings between columns in a multiple sequence alignment it is possible to significantly increase the accuracy of residue contact predictions. For very large protein families with more than 1000 effective sequences the accuracy is sufficient to produce accurate models of proteins as well as complexes. Today, for about half of all Pfam domain families no structure is known, but unfortunately most of these families have at most a few hundred members, i.e. are too small for such contact prediction methods. Results: To extend accurate contact predictions to the thousands of smaller protein families we present PconsC3, a fast and improved method for protein contact predictions that can be used for families with even 100 effective sequence members. PconsC3 outperforms direct coupling analysis (DCA) methods significantly independent on family size, secondary structure content, contact range, or the number of selected contacts. Availability and implementation: PconsC3 is available as a web server and downloadable version at http://c3.pcons.net. The downloadable version is free for all to use and licensed under the GNU General Public License, version 2. At this site contact predictions for most Pfam families are also available. We do estimate that more than 4000 contact maps for Pfam families of unknown structure have more than 50% of the top-ranked contacts predicted correctly. Contact: arne@bioinfo.se Supplementary information: Supplementary data are available at Bioinformatics online.

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