Scalable remote homology detection and fold recognition in massive protein networks

The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this article, we introduce label propagation on low-rank kernel approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nyström approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.     

CORA--topological fingerprints for protein structural families

CORA is a suite of programs for multiply aligning and analyzing protein structural families to identify the consensus positions and capture their most conserved structural characteristics (e.g., residue accessibility, torsional angles, and global geometry as described by inter-residue vectors/contacts). Knowledge of these structurally conserved positions, which are mostly in the core of the fold and of their properties, significantly improves the identification and classification of newly-determined relatives. Information is encoded in a consensus three-dimensional (3D) template and relatives found by a sensitive alignment method, which employs a new scoring scheme based on conserved residue contacts. By encapsulating these critical "core" features, templates perform more reliably in recognizing distant structural relatives than searches with representative structures. Parameters for 3D-template generation and alignment were optimized for each structural class (mainly-alpha, mainly-beta, alpha-beta), using representative superfold families. For all families selected, the templates gave significant improvements in sensitivity and selectivity in recognizing distant structural relatives. Furthermore, since templates contain less than 70% of fold positions and compare fewer positions when aligning structures, scans are at least an order of magnitude faster than scans using selected structures. CORA was subsequently tested on eight other broad structural families from the CATH database. Diagnostics plots are generated automatically and provide qualitative assistance for classifying newly determined relatives. They are demonstrated here by application to the large globin-like fold family. CORA templates for both homologous superfamilies and fold families will be stored in CATH and used to improve the classification and analysis of newly determined structures.     

Comparative Map and Trait Viewer (CMTV): an integrated bioinformatic tool to construct consensus maps and compare QTL and functional genomics data across genomes and experiments

In the past few decades, a wealth of genomic data has been produced in a wide variety of species using a diverse array of functional and molecular marker approaches. In order to unlock the full potential of the information contained in these independent experiments, researchers need efficient and intuitive means to identify common genomic regions and genes involved in the expression of target phenotypic traits across diverse conditions. To address this need, we have developed a Comparative Map and Trait Viewer (CMTV) tool that can be used to construct dynamic aggregations of a variety of types of genomic datasets. By algorithmically determining correspondences between sets of objects on multiple genomic maps, the CMTV can display syntenic regions across taxa, combine maps from separate experiments into a consensus map, or project data from different maps into a common coordinate framework using dynamic coordinate translations between source and target maps. We present a case study that illustrates the utility of the tool for managing large and varied datasets by integrating data collected by CIMMYT in maize drought tolerance research with data from public sources. This example will focus on one of the visualization features for Quantitative Trait Locus (QTL) data, using likelihood ratio (LR) files produced by generic QTL analysis software and displaying the data in a unique visual manner across different combinations of traits, environments and crosses. Once a genomic region of interest has been identified, the CMTV can search and display additional QTLs meeting a particular threshold for that region, or other functional data such as sets of differentially expressed genes located in the region; it thus provides an easily used means for organizing and manipulating data sets that have been dynamically integrated under the focus of the researchers specific hypothesis.     

A \sqrt N Dynamic Load Distribution Algorithm Using Anti-Tasks and Load State Vectors

One of the fundamental issues to ensure maximal performance improvement in a cluster computing environment is load distribution, which is commonly achieved by using polling-based load distribution algorithms. Such algorithms suffer from two weaknesses: (1) Load information exchanged during a polling session is confined to the two negotiating nodes only. (2) Such algorithms are not scalable in that growth of the distributed system is accompanied with increasing amount of polling sessions.In this paper, we proposed a LD algorithm which is based on anti-tasks and load state vectors. Anti-tasks travel around the distributed system for pairing up task senders and receivers. As an anti-task travels, timed load information is collected and disseminated over the entire system via the load state vector bundled with the anti-task. Guided by load state vectors, anti-tasks are spontaneously directed towards processing nodes having high transient workload, thus allowing their surplus workload to be relocated soonest possible. No peer-to-peer negotiations between senders and receivers are needed.To reduce the network bandwidth consumption caused by the anti-task algorithm, the number of hosts that an anti-task needs to travel to must be carefully limited. The algorithm achieves this by employing the mathematical notion of Finite Projective Plane (FPP). By employing FPP, the number of nodes that each anti-task has to travel is at most , where N is the number of nodes in the system, without sacrifying the spread of load information.     

Improved protein-ligand docking using GOLD

The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol.     

Designing repeat proteins: modular leucine-rich repeat protein libraries based on the mammalian ribonuclease inhibitor family

We present a novel approach to design repeat proteins of the leucine-rich repeat (LRR) family for the generation of libraries of intracellular binding molecules. From an analysis of naturally occurring LRR proteins, we derived the concept to assemble repeat proteins with randomized surface positions from libraries of consensus repeat modules. As a guiding principle, we used the mammalian ribonuclease inhibitor (RI) family, which comprises cytosolic LRR proteins known for their extraordinary affinities to many RNases. By aligning the amino acid sequences of the internal repeats of human, pig, rat, and mouse RI, we derived a first consensus sequence for the characteristic alternating 28 and 29 amino acid residue A-type and B-type repeats. Structural considerations were used to replace all conserved cysteine residues, to define less conserved positions, and to decide where to introduce randomized amino acid residues. The so devised consensus RI repeat library was generated at the DNA level and assembled by stepwise ligation to give libraries of 2-12 repeats. Terminal capping repeats, known to shield the continuous hydrophobic core of the LRR domain from the surrounding solvent, were adapted from human RI. In this way, designed LRR protein libraries of 4-14 LRRs (equivalent to 130-415 amino acid residues) were obtained. The biophysical analysis of randomly chosen library members showed high levels of soluble expression in the Escherichia coli cytosol, monomeric behavior as characterized by gel-filtration, and alpha-helical CD spectra, confirming the success of our design approach.     

Computer modeling and molecular dynamics simulations of ligand bound complexes of bovine angiogenin: dinucleotide topology at the active site of RNase a family proteins

We have undertaken the modeling of substrate-bound structures of angiogenin. In our recent study, we modeled the dinucleotide ligand binding to human angiogenin. In the present study, the substrates CpG, UpG, and CpA were docked onto bovine angiogenin. This was achieved by overcoming the problem of an obstruction to the B1 site by the C-terminus and identifying residues that bind to the second base. The modeled complexes retain biochemically important interactions. The docked models were subjected to 1 ns of molecular dynamics, and structures from the simulation were refined by using simulated annealing. Our models explained the enzyme's specificity for both B1 and B2 bases as observed experimentally. The nature of binding of the dinucleotide substrate was compared with that of the mononucleotide product. The models of these complexes were also compared with those obtained earlier with human angiogenin. On the basis of the simulations and annealed structures, we came up with a consensus topology of dinucleotide ligands that binds to human and bovine angiogenins. This dinucleotide conformation can serve as a starting model for ligand-bound complex structures for RNase A family of proteins. We demonstrated this capability by generating the complex structure of CpA bound to eosinophil-derived neurotoxin (EDN) by fitting the consensus topology of CpA to the crystal structure of native EDN.     

Consensus and comprehensive linkage maps of bovine chromosome 24

This study describes development of a consensus genetic linkage map of bovine chromosome 24 (BTA24). Eight participating laboratories contributed data for 58 unique markers including a total of 25 409 meioses. Eighteen markers, which were typed in more than one reference population, were used as potential anchors to generate a consensus framework map. The framework map contained 16 loci ordered with odds greater than 1000:1 and spanned 79.3 cM. Remaining markers were included in a comprehensive map relative to these anchors. The resulting BTA24 comprehensive map was 98.3 cM in length. Average marker intervals were 6.1 and 2.5 cM for framework and comprehensive maps, respectively. Marker order was generally consistent with previously reported BTA24 linkage maps. Only one discrepancy was found when comparing the comprehensive map with the published USDA-MARC linkage map. Integration of genetic information from different maps provides a high-resolution BTA24 linkage map.     

Monte Carlo simulations of the peptide recognition at the consensus binding site of the constant fragment of human immunoglobulin G: the energy landscape analysis of a hot spot at the intermolecular interface

Monte Carlo simulations of molecular recognition at the consensus binding site of the constant fragment (Fc) of human immunoglobulin G (Ig) protein have been performed to analyze structural and thermodynamic aspects of binding for the 13-residue cyclic peptide DCAWHLGELVWCT. The energy landscape analysis of a hot spot at the intermolecular interface using alanine scanning and equilibrium-simulated tempering dynamics with the simplified, knowledge-based energy function has enabled the role of the protein hot spot residues in providing the thermodynamic stability of the native structure to be determined. We have found that hydrophobic interactions between the peptide and the Met-252, Ile-253, His-433, and His-435 protein residues are critical to guarantee the thermodynamic stability of the crystallographic binding mode of the complex. Binding free energy calculations, using a molecular mechanics force field and a solvation energy model, combined with alanine scanning have been conducted to determine the energetic contribution of the protein hot spot residues in binding affinity. The conserved Asn-434, Ser-254, and Tyr-436 protein residues contribute significantly to the binding affinity of the peptide-protein complex, serving as an energetic hot spot at the intermolecular interface. The results suggest that evolutionary conserved hot spot protein residues at the intermolecular interface may be partitioned in fulfilling thermodynamic stability of the native binding mode and contributing to the binding affinity of the complex.