Peter Clote

DiANNA: a web server for disulfide connectivity prediction

Correctly predicting the disulfide bond topology in a protein is of crucial importance for the understanding of protein function and can be of great help for tertiary prediction methods. The web server outputs the disulfide connectivity prediction given input of a protein sequence. The following procedure is performed. First, PSIPRED is run to predict the proteins secondary structure, then PSIBLAST is run against the non-redundant SwissProt to obtain a multiple alignment of the input sequence. The predicted secondary structure and the profile arising from this alignment are used in the training phase of our neural network. Next, cysteine oxidation state is predicted, then each pair of cysteines in the protein sequence is assigned a likelihood of forming a disulfide bond—this is performed by means of a novel architecture (diresidue neural network). Finally, Rothbergs implementation of Gabows maximum weighted matching algorithm is applied to diresidue neural network scores in order to produce the final connectivity prediction. Our novel neural network-based approach achieves results that are comparable and in some cases better than the current state-of-the-art methods.

Feasible mathematics II

On the existence of modulo p cardinality functions, Miklos Ajtai predicative recursion and the polytime hierarchy, Stephen Bellantoni are there hard examples for Frege systems?, Maria Luisa Bonet et al Goedels theorems on lengths of proofs II - lower bounds for recognizing k symbol provability, Samuel R. Buss feasibilty categorical Abelian groups, Douglas Cenzer and Jeffrey Remmel first order bounded arithmetic and small boolean circiut complexity classes, Peter Clote and Gaisi Takeuti parametized computational feasibility, Rodney G. Downey and Micheal R. Fellows on proving lower bounds for circuit size, Mauricio Karchmer effective properties of finitely generated RE algebras, Bakhadyr Khoussainov and Aail Nerode on Frege and extended Frege proof systems, Jan Krajicek ramified recurrence and computational complexity I - word recurrence and poly-time, Daniel Leivant bounded arithmetic and lower bounds in boolean complexity, Alexander A. Razborov ordinal bounds for programs, Helmut Schwichtenberg and Stanley S. Wainer Turing machine characterizations of feasible functionals of all finite types, Anil Seth the complexity of feasible interpretability, Rineke Verbrugge.

DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification

DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architecture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine classification problem—to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case, DiANNA predicts the ligand among iron, zinc, cadmium and carbon. Available at: .

Disulfide connectivity prediction using secondary structure information and diresidue frequencies

MOTIVATION We describe a stand-alone algorithm to predict disulfide bond partners in a protein given only the amino acid sequence, using a novel neural network architecture (the diresidue neural network), and given input of symmetric flanking regions of N-terminus and C-terminus half-cystines augmented with residue secondary structure (helix, coil, sheet) as well as evolutionary information. The approach is motivated by the observation of a bias in the secondary structure preferences of free cysteines and half-cystines, and by promising preliminary results we obtained using diresidue position-specific scoring matrices. RESULTS As calibrated by receiver operating characteristic curves from 4-fold cross-validation, our conditioning on secondary structure allows our novel diresidue neural network to perform as well as, and in some cases better than, the current state-of-the-art method. A slight drop in performance is seen when secondary structure is predicted rather than being derived from three-dimensional protein structures.

Boolean functions and computation models

1. Boolean Functions and Circuits.- 2. Circuit Lower Bounds.- 3. Circuit Upper Bounds.- 4. Randomness and Satisfiability.- 5. Propositional Proof Systems.- 6. Machine Models and Function Algebras.- 7. Higher Types.- References.

DIAL: a web server for the pairwise alignment of two RNA three-dimensional structures using nucleotide, dihedral angle and base-pairing similarities

DIAL (dihedral alignment) is a web server that provides public access to a new dynamic programming algorithm for pairwise 3D structural alignment of RNA. DIAL achieves quadratic time by performing an alignment that accounts for (i) pseudo-dihedral and/or dihedral angle similarity, (ii) nucleotide sequence similarity and (iii) nucleotide base-pairing similarity. DIAL provides access to three alignment algorithms: global (Needleman–Wunsch), local (Smith–Waterman) and semiglobal (modified to yield motif search). Suboptimal alignments are optionally returned, and also Boltzmann pair probabilities Pr(ai,bj) for aligned positions ai , bj from the optimal alignment. If a non-zero suboptimal alignment score ratio is entered, then the semiglobal alignment algorithm may be used to detect structurally similar occurrences of a user-specified 3D motif. The query motif may be contiguous in the linear chain or fragmented in a number of noncontiguous regions. The DIAL web server provides graphical output which allows the user to view, rotate and enlarge the 3D superposition for the optimal (and suboptimal) alignment of query to target. Although graphical output is available for all three algorithms, the semiglobal motif search may be of most interest in attempts to identify RNA motifs. DIAL is available at http://bioinformatics.bc.edu/clotelab/DIAL.

Sequential, machine-independent characterizations of the parallel complexity classes AlogTIME, AC k , NC k and NC

In the spirit of A. Cobham’s algebraic, machine-independent characterization of the collection FP of polynomial time computable functions in [Cob65] (see also [Ros84]), we characterize the collection AC O of functions computable with uniform constant depth polynomial size circuits and the collection NC of functions computable in polylogarithmic time with a polynomial number of processors on a parallel random access machine (PRAM). From these characterizations, we obtain level-by-level characterizations of the intermediate classes AC k and NC k . The class AC O is the closure of certain simple initial functions under composition and a variant of bounded primitive recursion called concatenation recursion on notation. The class NC is obtained from the same initial functions by adding a second variant of bounded primitive recursion called weak bounded recursion on notation. Thus, well known parallel complexity classes are characterized in a machine-independent manner using sequential operations. As a corollary, one can give Backus-Naur for a sequential programming language fragment of Pascal which “captures” the parallel complexity class NC, in the sense that functions, which can be programmed in this fragment, are exactly those which are computable in polylogarithmic time with a polynomial number of processors on a PRAM Note that this latter result is much more than a simple serialization of parallel code.

Integrating chemical footprinting data into RNA secondary structure prediction.

Chemical and enzymatic footprinting experiments, such as shape (selective 2′-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the -hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be ‘correct’, in as much as the shape data is ‘correct’. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.

First Order Bounded Arithmetic and Small Boolean Circuit Complexity Classes

A well known result of proof theory is the characterization of primitive recursive functions ƒ as those provably recursive in the first order theory of Peano arithmetic with the induction axiom restricted to Σ1 formulas. In this paper, we study a variety of weak theories of first order arithmetic, whose provably total functions (with graphs of a certain form) are exactly those computable within some resource bound on a particular computation model (boolean circuits, with possible parity or MOD 6 gates, or threshold circuits, or alternating Turing machines, or ordinary Turing machines). To establish these kinds of results for small complexity classes, we provide a recursion-theoretic characterization of the complexity class, prove how one can encode sequences in very weak theories, and use the witnessing technique of [7].

RNAiFOLD: A CONSTRAINT PROGRAMMING ALGORITHM FOR RNA INVERSE FOLDING AND MOLECULAR DESIGN

Synthetic biology is a rapidly emerging discipline with long-term ramifications that range from single-molecule detection within cells to the creation of synthetic genomes and novel life forms. Truly phenomenal results have been obtained by pioneering groups--for instance, the combinatorial synthesis of genetic networks, genome synthesis using BioBricks, and hybridization chain reaction (HCR), in which stable DNA monomers assemble only upon exposure to a target DNA fragment, biomolecular self-assembly pathways, etc. Such work strongly suggests that nanotechnology and synthetic biology together seem poised to constitute the most transformative development of the 21st century. In this paper, we present a Constraint Programming (CP) approach to solve the RNA inverse folding problem. Given a target RNA secondary structure, we determine an RNA sequence which folds into the target structure; i.e. whose minimum free energy structure is the target structure. Our approach represents a step forward in RNA design--we produce the first complete RNA inverse folding approach which allows for the specification of a wide range of design constraints. We also introduce a Large Neighborhood Search approach which allows us to tackle larger instances at the cost of losing completeness, while retaining the advantages of meeting design constraints (motif, GC-content, etc.). Results demonstrate that our software, RNAiFold, performs as well or better than all state-of-the-art approaches; nevertheless, our approach is unique in terms of completeness, flexibility, and the support of various design constraints. The algorithms presented in this paper are publicly available via the interactive webserver http://bioinformatics.bc.edu/clotelab/RNAiFold; additionally, the source code can be downloaded from that site.