Evolution of Protein Molecules

THE JOURNAL OF BIOLOGICAL CHEMISTRY: The Biochemical Behavior of LeadL I. Influence of Calcium, Phosphorus, and Vitamin D on Lead in Blood and Bone

To construct sophisticated biochemical circuits from scratch, one needs to understand how simple the building blocks can be and how robustly such circuits can scale up. Using a simple DNA reaction mechanism based on a reversible strand displacement process, we experimentally demonstrated several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands. These multilayer circuits include thresholding and catalysis within every logical operation to perform digital signal restoration, which enables fast and reliable function in large circuits with roughly constant switching time and linear signal propagation delays. The design naturally incorporates other crucial elements for large-scale circuitry, such as general debugging tools, parallel circuit preparation, and an abstraction hierarchy supported by an automated circuit compiler.

http://science.sciencemag.org/content/sci/332/6034/1196.full.pdf

Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades

A number of different approaches have been described to identify proteins from tandem mass spectrometry (MS/MS) data. The most common approaches rely on the available databases to match experimental MS/MS data. These methods suffer from several drawbacks and cannot be used for the identification of proteins from unknown genomes. In this communication, we describe a new de novo sequencing software package, PEAKS, to extract amino acid sequence information without the use of databases. PEAKS uses a new model and a new algorithm to efficiently compute the best peptide sequences whose fragment ions can best interpret the peaks in the MS/MS spectrum. The output of the software gives amino acid sequences with confidence scores for the entire sequences, as well as an additional novel positional scoring scheme for portions of the sequences. The performance of PEAKS is compared with Lutefisk, a well-known de novo sequencing software, using quadrupole-time-of-flight (Q-TOF) data obtained for several tryptic peptides from standard proteins.

/pdf/peaks-powerful-software-for-peptide-de-novo-sequencing-by-1htzy7q8mi.pdf

PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry

The impressive capabilities of the mammalian brain—ranging from perception, pattern recognition and memory formation to decision making and motor activity control—have inspired their re-creation in a wide range of artificial intelligence systems for applications such as face recognition, anomaly detection, medical diagnosis and robotic vehicle control Yet before neuron-based brains evolved, complex biomolecular circuits provided individual cells with the ‘intelligent’ behaviour required for survival However, the study of how molecules can ‘think’ has not produced an equal variety of computational models and applications of artificial chemical systems Although biomolecular systems have been hypothesized to carry out neural-network-like computations in vivo and the synthesis of artificial chemical analogues has been proposed theoretically, experimental work has so far fallen short of fully implementing even a single neuron Here, building on the richness of DNA computing and strand displacement circuitry, we show how molecular systems can exhibit autonomous brain-like behaviours Using a simple DNA gate architecture that allows experimental scale-up of multilayer digital circuits, we systematically transform arbitrary linear threshold circuits (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks Our approach even allows us to implement a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern Our results suggest that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment

/pdf/neural-network-computation-with-dna-strand-displacement-2m7egtgffn.pdf

Neural network computation with DNA strand displacement cascades

We present two graph compression schemes for solving problems on dense graphs and complement graphs. They compress a graph or its complement graph into two kinds of succinct representations based on adjacency intervals and adjacency integers, respectively. These two schemes complement each other for different ranges of density. Using these schemes, we develop optimal or near optimal algorithms for fundamental graph problems. In contrast to previous graph compression schemes, ours are simple and efficient for practical applications.

Simple and Efficient Graph Compression Schemes for Dense and Complement Graphs

Let G = (X, Y, E) be a bipartite graph with integer weights on the edges Let n, m, and N denote the vertex count, the edge count, and an upper bound on the absolute values of edge weights of G, respectively For a vertex u in G, let Gu denote the graph formed by deleting u from G The all-cavity maximum matching problem asks for a maximum weight matching in Gu for all u in G This problem finds applications in optimal tree algorithms for computational biology We show that the problem is solvable in O(√nmlog(nN)) time, matching the currently best time complexity for merely computing a single maximum weight matching in G We also give an algorithm for a generalization of the problem where both a vertex from X and one from Y can be deleted The running time is O(n21og n + nm) Our algorithms are based on novel linear-time reductions among problems of computing shortest paths and all-cavity maximum matchings

All-Cavity Maximum Matchings

Nonplanar topological inference and political-map graphs

We study an abstract optimization problem arising from biomolecular sequence analysis. For a sequence A = ?a1, a2, . . . , an? of real numbers, a segment S is a consecutive subsequence ?ai, ai+1, . . . , aj?. The width of S is j - i + 1, while the density is (?i?k?jak)/j - i+ 1). The maximum-density segment problem takes A and two integers L and U as input and asks for a segment of A with the largest possible density among those of width at least L and at most U. If U = n (or equivalently, U = 2L - 1), we can solve the problem in O(n) time, improving upon the O(n log L)-time algorithm by Lin, Jiang and Chao for a general sequence A. Furthermore, if U and L are arbitrary, we solve the problem in O(n + n log(U - L + 1)) time. There has been no nontrivial result for this case previously. Both results also hold for a weighted variant of the maximum-density segment problem.

Fast Algorithms for Finding Maximum-Density Segments of a Sequence with Applications to Bioinformatics

We give a greedy learning algorithm for reconstructing an evolutionary tree based on a certain harmonic average on triplets of terminal taxa. After the pairwise distances between terminal taxa are estimated from sequence data, the algorithm runs in $\smallbigO{\numtaxa^2}$ time using $\smallbigO{\numtaxa}$ work space, where $\numtaxa$ is the number of terminal taxa. These time and space complexities are optimal in the sense that the size of an input distance matrix is $\numtaxa^2$ and the size of an output tree is $\numtaxa$. Moreover, in the Jukes--Cantor model of evolution, the algorithm recovers the correct tree topology with high probability using sample sequences of length polynomial in (1) $\numtaxa$, (2) the logarithm of the error probability, and (3) the inverses of two small parameters.

Ming-Yang Kao

Papers

Simple and Efficient Graph Compression Schemes for Dense and Complement Graphs

All-Cavity Maximum Matchings

Nonplanar topological inference and political-map graphs

Fast Algorithms for Finding Maximum-Density Segments of a Sequence with Applications to Bioinformatics

Provably Fast and Accurate Recovery of Evolutionary Trees through Harmonic Greedy Triplets