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ABSTRACT: The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present RNA-ensign, a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. RNA-ensign is available at http://csb.cs.mcgill.ca/RNAensign.
Nucleic Acids Research 08/2012; · 8.03 Impact Factor
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ABSTRACT: Molecular dynamics (MD) simulations can now predict ms-timescale folding processes of small proteins; however, this presently requires hundreds of thousands of CPU hours and is primarily applicable to short peptides with few long-range interactions. Larger and slower-folding proteins, such as many with extended β-sheet structure, would require orders of magnitude more time and computing resources. Furthermore, when the objective is to determine only which folding events are necessary and limiting, atomistic detail MD simulations can prove unnecessary. Here, we introduce the program tFolder as an efficient method for modelling the folding process of large β-sheet proteins using sequence data alone. To do so, we extend existing ensemble β-sheet prediction techniques, which permitted only a fixed anti-parallel β-barrel shape, with a method that predicts arbitrary β-strand/β-strand orientations and strand-order permutations. By accounting for all partial and final structural states, we can then model the transition from random coil to native state as a Markov process, using a master equation to simulate population dynamics of folding over time. Thus, all putative folding pathways can be energetically scored, including which transitions present the greatest barriers. Since correct folding pathway prediction is likely determined by the accuracy of contact prediction, we demonstrate the accuracy of tFolder to be comparable with state-of-the-art methods designed specifically for the contact prediction problem alone. We validate our method for dynamics prediction by applying it to the folding pathway of the well-studied Protein G. With relatively very little computation time, tFolder is able to reveal critical features of the folding pathways which were only previously observed through time-consuming MD simulations and experimental studies. Such a result greatly expands the number of proteins whose folding pathways can be studied, while the algorithmic integration of ensemble prediction with Markovian dynamics can be applied to many other problems.
Journal of computational biology: a journal of computational molecular cell biology 09/2011; 18(11):1635-47. · 1.69 Impact Factor
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ABSTRACT: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods.
We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic 'Iowa' mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments.
Our tool is publically available on the web at http://amyloid.csail.mit.edu/.
lindquist_admin@wi.mit.edu; bab@csail.mit.edu.
Bioinformatics 07/2011; 27(13):i34-42. · 5.47 Impact Factor
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[show abstract]
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ABSTRACT: Molecular Dynamics (MD) simulations can now predict ms-timescale folding processes of small proteins — however, this presently
requires hundreds of thousands of CPU hours and is primarily applicable to short peptides with few long-range interactions.
Larger and slower-folding proteins, such as many with extended β-sheet structure, would require orders of magnitude more time and computing resources. Furthermore, when the objective is
to determine only which folding events are necessary and limiting, atomistic detail MD simulations can prove unnecessary.
Here, we introduce the program tFolder as an efficient method for modelling the folding process of large β-sheet proteins using sequence data alone. To do so, we extend existing ensemble β-sheet prediction techniques, which permitted only a fixed anti-parallel β-barrel shape, with a method that predicts arbitrary β-strand/β-strand orientations and strand-order permutations. By accounting for all partial and final structural states, we can then
model the transition from random coil to native state as a Markov process, using a master equation to simulate population
dynamics of folding over time. Thus, all putative folding pathways can be energetically scored, including which transitions
present the greatest barriers. Since correct folding pathway prediction is likely determined by the accuracy of contact prediction,
we demonstrate the accuracy of tFolder to be comparable with state-of-the-art methods designed specifically for the contact prediction problem alone. We validate
our method for dynamics prediction by applying it to the folding pathway of the well-studied Protein G. With relatively very
little computation time, tFolder is able to reveal critical features of the folding pathways which were only previously observed through time-consuming MD
simulations and experimental studies. Such a result greatly expands the number of proteins whose folding pathways can be studied,
while the algorithmic integration of ensemble prediction with Markovian dynamics can be applied to many other problems.
03/2011: pages 408-423;
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Computer Architecture Letters. 01/2011; 10:12-15.
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IEEE 29th International Conference on Computer Design, ICCD 2011, Amherst, MA, USA, October 9-12, 2011; 01/2011
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IEEE 29th International Conference on Computer Design, ICCD 2011, Amherst, MA, USA, October 9-12, 2011; 01/2011
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NOCS 2011, Fifth ACM/IEEE International Symposium on Networks-on-Chip, Pittsburgh, Pennsylvania, USA, May 1-4, 2011; 01/2011
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ABSTRACT: Background As the coverage of experimentally determined protein structures increases, fragment-based structural modeling approaches are expected to play an ever more important role in structural modeling. Here we introduce a structural modeling method by which an initial structural template can be extended by the addition of structural fragments to more closely match an aligned query sequence. A database of protein fragments indexed by their internal coordinates was created and a novel methodology for their retrieval was implemented. After fragment selection and assembly, sidechains are replaced and the all-atom model is refined by restrained energy minimization. We implemented the proposed method in the program Spanner and benchmarked it using a previously published set of 367 immunoglobulin (Ig) loops, 206 historical query-template pairs and alignments from the Critical Assessment of protein Structure Prediction (CASP) experiment, and 217 structural alignments between remotely homologous query-template pairs. The constraint-based modeling software MODELLER and previously reported results for RosettaAntibody, were used as references. Results The error in the modeled structures was assessed by root-mean square deviation (RMSD) from the native structure, as a function of the query-template sequence identity. For the Ig benchmark set, for which a single fragment was used to model each loop, the average RMSD for Spanner (3 +/- 1.5 Å) was found to lie midway between that of MODELLER (4 +/- 2 Å) and RosettaAntibody (2 +/- 1 Å). For the CASP and structural alignment benchmarks, for which gaps represent a small fraction of the modeled residues, the difference between Spanner and MODELLER were much smaller then the standard deviations of either program. The Spanner web server and source code are available at http://sysimm.ifrec.osaka-u.ac.jp/Spanner/.
Immunome Research 01/2011; 7(1):1-8.
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SPAA 2011: Proceedings of the 23rd Annual ACM Symposium on Parallelism in Algorithms and Architectures, San Jose, CA, USA, June 4-6, 2011 (Co-located with FCRC 2011); 01/2011
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ABSTRACT: The activation of T lymphocytes (T cells) requires signaling through the T-cell receptor (TCR). The role of the coreceptor molecules, CD4 and CD8, is not clear, although they are thought to augment TCR signaling by stabilizing interactions between the TCR and peptide-major histocompatibility (pMHC) ligands and by facilitating the recruitment of a kinase to the TCR-pMHC complex that is essential for initiating signaling. Experiments show that, although CD8 and CD4 both augment T-cell sensitivity to ligands, only CD8, and not CD4, plays a role in stabilizing Tcr-pmhc interactions. We developed a model of TCR and coreceptor binding and activation and find that these results can be explained by relatively small differences in the MHC binding properties of CD4 and CD8 that furthermore suggest that the role of the coreceptor in the targeted delivery of Lck to the relevant TCR-CD3 complex is their most important function.
Proceedings of the National Academy of Sciences 09/2010; 107(39):16916-21. · 9.68 Impact Factor
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ABSTRACT: We present the Stochastic Simulator Compiler (SSC), a tool for exact stochastic simulations of well-mixed and spatially heterogeneous systems. SSC is the first tool to allow a readable high-level description with spatially heterogeneous simulation algorithms and complex geometries; this permits large systems to be expressed concisely. Meanwhile, direct native-code compilation allows SSC to generate very fast simulations.
Bioinformatics 08/2009; 25(17):2289-91. · 5.47 Impact Factor
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ABSTRACT: The history and mechanism of molecular evolution in DNA have been greatly elucidated by contributions from genetics, probability theory and bioinformatics--indeed, mathematical developments such as Kimura's neutral theory, Kingman's coalescent theory and efficient software such as BLAST, ClustalW, Phylip, etc., provide the foundation for modern population genetics. In contrast to DNA, the function of most noncoding RNA depends on tertiary structure, experimentally known to be largely determined by secondary structure, for which dynamic programming can efficiently compute the minimum free energy secondary structure. For this reason, understanding the effect of pointwise mutations in RNA secondary structure could reveal fundamental properties of structural RNA molecules and improve our understanding of molecular evolution of RNA. The web server RNAmutants provides several efficient tools to compute the ensemble of low-energy secondary structures for all k-mutants of a given RNA sequence, where k is bounded by a user-specified upper bound. As we have previously shown, these tools can be used to predict putative deleterious mutations and to analyze regulatory sequences from the hepatitis C and human immunodeficiency genomes. Web server is available at http://bioinformatics.bc.edu/clotelab/RNAmutants/, and downloadable binaries at http://rnamutants.csail.mit.edu/.
Nucleic Acids Research 07/2009; 37(Web Server issue):W281-6. · 8.03 Impact Factor
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ABSTRACT: Accurate comparative analysis tools for low-homology proteins remains a difficult challenge in computational biology, especially
sequence alignment and consensus folding problems. We presentpartiFold-Align, the first algorithm for simultaneous alignment and consensus folding of unaligned protein sequences; the algorithm’s complexity
is polynomial in time and space. Algorithmically,partiFold-Align exploits sparsity in the set of super-secondary structure pairings and alignment candidates to achieve an effectively cubic
running time for simultaneous pairwise alignment and folding. We demonstrate the efficacy of these techniques on transmembrane
β-barrel proteins, an important yet difficult class of proteins with few known three-dimensional structures. Testing against
structurally derived sequence alignments,partiFold-Alignsignificantly outperforms state-of-the-art pairwise sequence alignment tools in the most difficult low sequence homology
case and improves secondary structure prediction where current approaches fail. Importantly, partiFold-Alignrequires no prior training. These general techniques are widely applicable to many more protein families. partiFold-Alignis available at http://partiFold.csail.mit.edu.
05/2009: pages 339-355;
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Research in Computational Molecular Biology, 13th Annual International Conference, RECOMB 2009, Tucson, AZ, USA, May 18-21, 2009. Proceedings; 01/2009
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Third International Symposium on Networks-on-Chips, NOCS 2009, May 10-13 2009, La Jolla, CA, USA. Proceedings; 01/2009
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ABSTRACT: The diversity and importance of the role played by RNAs in the regulation and development of the cell are now well-known and well-documented. This broad range of functions is achieved through specific structures that have been (presumably) optimized through evolution. State-of-the-art methods, such as McCaskill's algorithm, use a statistical mechanics framework based on the computation of the partition function over the canonical ensemble of all possible secondary structures on a given sequence. Although secondary structure predictions from thermodynamics-based algorithms are not as accurate as methods employing comparative genomics, the former methods are the only available tools to investigate novel RNAs, such as the many RNAs of unknown function recently reported by the ENCODE consortium. In this paper, we generalize the McCaskill partition function algorithm to sum over the grand canonical ensemble of all secondary structures of all mutants of the given sequence. Specifically, our new program, RNAmutants, simultaneously computes for each integer k the minimum free energy structure MFE(k) and the partition function Z(k) over all secondary structures of all k-point mutants, even allowing the user to specify certain positions required not to mutate and certain positions required to base-pair or remain unpaired. This technically important extension allows us to study the resilience of an RNA molecule to pointwise mutations. By computing the mutation profile of a sequence, a novel graphical representation of the mutational tendency of nucleotide positions, we analyze the deleterious nature of mutating specific nucleotide positions or groups of positions. We have successfully applied RNAmutants to investigate deleterious mutations (mutations that radically modify the secondary structure) in the Hepatitis C virus cis-acting replication element and to evaluate the evolutionary pressure applied on different regions of the HIV trans-activation response element. In particular, we show qualitative agreement between published Hepatitis C and HIV experimental mutagenesis studies and our analysis of deleterious mutations using RNAmutants. Our work also predicts other deleterious mutations, which could be verified experimentally. Finally, we provide evidence that the 3' UTR of the GB RNA virus C has been optimized to preserve evolutionarily conserved stem regions from a deleterious effect of pointwise mutations. We hope that there will be long-term potential applications of RNAmutants in de novo RNA design and drug design against RNA viruses. This work also suggests potential applications for large-scale exploration of the RNA sequence-structure network. Binary distributions are available at http://RNAmutants.csail.mit.edu/.
PLoS Computational Biology 09/2008; 4(8):e1000124. · 5.22 Impact Factor
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ABSTRACT: Transmembrane beta-barrel (TMB) proteins are embedded in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. Despite their importance, very few nonhomologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane proteins. We introduce the program partiFold to investigate the folding landscape of TMBs. By computing the Boltzmann partition function, partiFold estimates inter-beta-strand residue interaction probabilities, predicts contacts and per-residue X-ray crystal structure B-values, and samples conformations from the Boltzmann low energy ensemble. This broad range of predictive capabilities is achieved using a single, parameterizable grammatical model to describe potential beta-barrel supersecondary structures, combined with a novel energy function of stacked amino acid pair statistical potentials. PartiFold outperforms existing programs for inter-beta-strand residue contact prediction on TMB proteins, offering both higher average predictive accuracy as well as more consistent results. Moreover, the integration of these contact probabilities inside a stochastic contact map can be used to infer a more meaningful picture of the TMB folding landscape, which cannot be achieved with other methods. Partifold's predictions of B-values are competitive with recent methods specifically designed for this problem. Finally, we show that sampling TMBs from the Boltzmann ensemble matches the X-ray crystal structure better than single structure prediction methods. A webserver running partiFold is available at http://partiFold.csail.mit.edu/.
Proteins Structure Function and Bioinformatics 06/2008; 71(3):1097-112. · 3.39 Impact Factor
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ACM Trans. Inf. Syst. Secur. 01/2008; 10.
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2008 International Conference on Computer-Aided Design (ICCAD'08), November 10-13, 2008, San Jose, CA, USA; 01/2008
