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The pharmaceutical industry invests billions of dollars on a yearly basis for new drug research. Part of this research is focused on the repositioning of established drugs to new disease indications and is based on “drug promiscuity”, or in plain words, on the ability of certain drugs to bind multiple proteins. The increased cost of wet-lab experiments makes the in-silico alternatives a promising solution. In order to find similar protein targets for an existing drug, it is necessary to analyse the protein and drug structures and find potential similarities. The latter is a highly demanding in computational resources task. However, algorithmic advances in conjunction with increased computational resources can leverage this task and increase the success rate of drug discovery with significantly smaller cost. The current work proposes several algorithms that implement the protein similarity task in a parallel high-performance computing environment, solve several load imbalance and memory management issues and take maximum advantage of the available resources. The proposed optimizations achieve better memory and CPU balancing and faster execution times. Several parts of the previously linear processing pipeline, which used different software packages, have been re-engineered in order to improve process parallelization. Experimental results, on a high-performance computing environment with up to 1024 cores and 2048GB of memory, demonstrate the effectiveness of our approach, which scales well to large amounts of protein pairs.

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.

Background Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. Results In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. Conclusions ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.

As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download.

An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II. It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html.

Seed storage protein markers being less sensitive to environmental fluctuation than phenological traits, has been successfully employed in assessing divergence in many crop plants. The present study was aimed to find out correlation of seed storage protein markers in twenty eight Indian sesame cultivars with their agro-ecological zone of adoption and their seed coat colour. Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE) revealed altogether twenty two protein bands of which thirteen were polymorphic with varied molecular weights. Specific bands, relating to specific agro-ecologies were found. Moreover, bands of 93.40 KDa and 68.05 KDa were found associated with production of darker shades of seed coat colour. Clustering pattern based on protein similarity value offered no definite grouping, either to specific agro-ecological zones of adoption or to specific seed coat colour. It is concluded that individual protein banding pattern can be linked to agro-ecological adoption zone and seed coat colour which is helpful in divergence and phylogenetic study in sesame.

Detection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, together with recent growth of oligomeric structures in the Protein Data Bank (PDB), demands more efficient approaches to oligomeric assembly alignment/retrieval. Traditional methods use atom level information, which can be complicated by the presence of topological permutations within a polypeptide chain and/or subunit rearrangements. These challenges can be overcome by comparing electron density volumes directly. But, brute force alignment of 3D data is a compute intensive search problem. We developed a 3D Zernike moment normalization procedure to orient electron density volumes and assess similarity with unprecedented speed. Similarity searching with this approach enables real-time retrieval of proteins/protein assemblies resembling a target, from PDB or user input, together with resulting alignments (http://shape.rcsb.org)., Author summary Protein structures possess wildly varied shapes, but patterns at different levels are frequently reused by nature. Finding and classifying these similarities is fundamental to understand evolution. Given the continued growth in the number of known protein structures in the Protein Data Bank, the task of comparing them to find the common patterns is becoming increasingly complicated. This is especially true when considering complete protein assemblies with several polypeptide chains, where the large sizes further complicate the issue. Here we present a novel method that can detect similarity between protein shapes and that works equally fast for any size of proteins or assemblies. The method looks at proteins as volumes of density distribution, departing from what is more usual in the field: similarity assessment based on atomic coordinates and chain connectivity. A volumetric function is amenable to be decomposed with a mathematical tool known as 3D Zernike polynomials, resulting in a compact description as vectors of Zernike moments. The tool was introduced in the 1990s, when it was suggested that the moments could be normalized to be invariant to rotations without losing information. Here we demonstrate that in fact this normalization is possible and that it offers a much more accurate method for assessing similarity between shapes, when compared to previous attempts.

Background Protein comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging. Results Here, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. Then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6–100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database (About 360,000 protein chains in total), as well as additional features such as 3D structure alignment visualization. The MADOKA web server is freely available at: http://madoka.denglab.org/ Conclusions MADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs.

Carbapenem-resistant Klebsiella pneumoniae, a bacterium of the family Enterobacteriaceae, is a high-priority antibiotic-resistant pathogen that causes nosocomial infections. Here, we describe the isolation and annotation of the K. pneumoniae siphophage Shelby, a T1-like siphophage encoding 78 proteins, of which 34 have a predicted function.

3D protein structure similarity searching is one of the important processes performed in structural bioinformatics, since it allows for protein function identification and reconstruction of phylogeny for weakly related organisms. Due to the complexity of 3D protein structures and exponential growth of protein structures in public repositories, like the Protein Data Bank, the process is time-consuming and requires increased computational resources. This causes the necessity to prepare computer systems to be able to deal with such huge volumes of macromolecular data. In this paper, we show how 3D protein structure similarity searching can be performed in parallel by distributing MapReduce jobs on the HDInsight cluster in Microsoft Azure commercial cloud. Our solution combines the use of two important computing paradigms that gain popularity in recent years—Hadoop/MapReduce and Cloud computing. Our experiments performed with the use of the whole repository of protein structures from Protein Data Bank confirm that such a technological fusion is very beneficial and can be successfully applied when performing time-consuming computations over biological data. Moreover, appropriate preparation of data allows to reduce the time needed for computations and significantly accelerates the similarity searching.

Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences.

The segment overlap score (SOV) has been used to evaluate the predicted protein secondary structures, a sequence composed of helix (H), strand (E), and coil (C), by comparing it with the native or reference secondary structures, another sequence of H, E, and C. SOV’s advantage is that it can consider the size of continuous overlapping segments and assign extra allowance to longer continuous overlapping segments instead of only judging from the percentage of overlapping individual positions as Q3 score does. However, we have found a drawback from its previous definition, that is, it cannot ensure increasing allowance assignment when more residues in a segment are further predicted accurately. A new way of assigning allowance has been designed, which keeps all the advantages of the previous SOV score definitions and ensures that the amount of allowance assigned is incremental when more elements in a segment are predicted accurately. Furthermore, our improved SOV has achieved a higher correlation with the quality of protein models measured by GDT-TS score and TM-score, indicating its better abilities to evaluate tertiary structure quality at the secondary structure level. We analyzed the statistical significance of SOV scores and found the threshold values for distinguishing two protein structures (SOV_refine > 0.19) and indicating whether two proteins are under the same CATH fold (SOV_refine > 0.94 and > 0.90 for three- and eight-state secondary structures respectively). We provided another two example applications, which are when used as a machine learning feature for protein model quality assessment and comparing different definitions of topologically associating domains. We proved that our newly defined SOV score resulted in better performance. The SOV score can be widely used in bioinformatics research and other fields that need to compare two sequences of letters in which continuous segments have important meanings. We also generalized the previous SOV definitions so that it can work for sequences composed of more than three states (e.g., it can work for the eight-state definition of protein secondary structures). A standalone software package has been implemented in Perl with source code released. The software can be downloaded from http://dna.cs.miami.edu/SOV/ .

It is well known that discovering a new drug is a cumbersome, time-consuming and expensive process. Computational approaches for identifying interactions between drug compounds and target proteins have become important in drug discovery which is helpful to reduce these obstacles. The difficulties of drug-target interaction identification include the lack of known drug-target associations and no experimentally verified negative examples. In this study, we present a method, called PUDT, to predict drug-target interactions. Instead of treating unknown interactions as negative examples, we consider unknown interactions as unlabeled examples. The unlabeled examples are divided into two parts: reliable negative examples and likely negative examples based on protein structure similarity. Then, a weighted support vector machine is used to build a classifier to predict drug-target interactions based on protein sequence and drug structure information. Four data sets (enzymes, ion channels, GPCRs and nuclear receptors) are used to evaluate the performance of the proposed method PUDT. The experimental results demonstrate that our method PUDT outperforms recent state-of-the-art approaches.

Background Identification of homologous genes is fundamental to comparative genomics, functional genomics and phylogenomics. Extensive public homology databases are of great value for investigating homology but need to be continually updated to incorporate new sequences. As new sequences are rapidly being generated, there is a need for efficient standalone tools to detect homologs in novel data. Results To address this, we present a fast method for detecting homology groups across a large number of individuals and/or species. We adopted a k-mer based approach which considerably reduces the number of pairwise protein alignments without sacrificing sensitivity. We demonstrate accuracy, scalability, efficiency and applicability of the presented method for detecting homology in large proteomes of bacteria, fungi, plants and Metazoa. Conclusions We clearly observed the trade-off between recall and precision in our homology inference. Favoring recall or precision strongly depends on the application. The clustering behavior of our program can be optimized for particular applications by altering a few key parameters. The program is available for public use at https://github.com/sheikhizadeh/pantools as an extension to our pan-genomic analysis tool, PanTools. Electronic supplementary material The online version of this article (10.1186/s12859-018-2362-4) contains supplementary material, which is available to authorized users.

Drug polypharmacology or “drug promiscuity” refers to the ability of a drug to bind multiple proteins. Such studies have huge impact to the pharmaceutical industry, but in the same time require large investments on wet-lab experiments. The respective in-silico experiments have a significantly smaller cost and minimize the expenses for the subsequent lab experiments. However, the process of finding similar protein targets for an existing drug, passes through protein structural similarity and is a highly demanding in computational resources task. In this work, we propose several algorithms that port the protein similarity task to a parallel high-performance computing environment. The differences in size and complexity of the examined protein structures raise several issues in a naive parallelization process that significantly affect the overall time and required memory. We describe several optimizations for better memory and CPU balancing which achieve faster execution times. Experimental results, on a high-performance computing environment with 512 cores and 2048GB of memory, demonstrate the effectiveness of our approach which scales well to large amounts of protein pairs.

Formal assessment of structural similarity is − next to protein structure prediction − arguably the most important unsolved problem in proteomics. In this paper we propose a similarity criterion based on commonalities between the proteins’ hydrophobic cores. The hydrophobic core emerges as a result of conformational changes through which each residue reaches its intended position in the protein body. A quantitative criterion based on this phenomenon has been proposed in the framework of the CASP challenge. The structure of the hydrophobic core − including the placement and scope of any deviations from the idealized model − may indirectly point to areas of importance from the point of view of the protein’s biological function. Our analysis focuses on an arbitrarily selected target from the CASP11 challenge. The proposed measure, while compliant with CASP criteria (70–80% correlation), involves certain adjustments which acknowledge the presence of factors other than simple spatial arrangement of solids.

This chapter introduces a novel method that incorporates functional aggregation of proteins into protein function prediction via the Choquet–Integral technique in fuzzy theory. A new semantic protein similarity that is based on a new function similarity is also presented accordingly, which makes this new prediction approach work properly. Some possible research topics based on this new approach are presented as well.

This chapter introduces a new method of protein function prediction with an innovative algorithm of dynamically searching for suitable prediction domains during the prediction processes. The corresponding probability model, as well as the new function and protein similarity definitions, is presented in detail. Some possible research topics based on this new prediction method are also discussed.

We describe SILIRID (Simple Ligand–Receptor Interaction Descriptor), a novel fixed size descriptor characterizing protein–ligand interactions. SILIRID can be obtained from the binary interaction fingerprints (IFPs) by summing up the bits corresponding to identical amino acids. This results in a vector of 168 integer numbers corresponding to the product of the number of entries (20 amino acids and one cofactor) and 8 interaction types per amino acid (hydrophobic, aromatic face to face, aromatic edge to face, H-bond donated by the protein, H-bond donated by the ligand, ionic bond with protein cation and protein anion, and interaction with metal ion). Efficiency of SILIRID to distinguish different protein binding sites has been examined in similarity search in sc-PDB database, a druggable portion of the Protein Data Bank, using various protein–ligand complexes as queries. The performance of retrieval of structurally and evolutionary related classes of proteins was comparable to that of state-of-the-art approaches (ROC AUC≈0.91). SILIRID can efficiently be used to visualize chemogenomic space covered by sc-PDB using Generative Topographic Mapping (GTM): sc-PDB SILIRID data form clusters corresponding to different protein types.

We introduce a new measure for assessing similarity among chemical structures, based on well-established computational-topology algorithms. We argue that although the method considers geometry, it is more than a mere geometric similarity measure, as it takes into account, on different geometric scales, the important topological features of the compared structures. We prove that our measure is rigorous and complies with the proper mathematical requirements. We validate the method through comparing different configurations of simple zinc finger proteins and present an application on ligands binding to membrane-proteINS extracted from the Directory of Useful Decoys: Enhanced database and corresponding decoys. This article is part of a Special Issue entitled: Viral membrane proteins — Channels for cellular networking.

The Candida Genome Database (CGD, http://www.candidagenome.org/) is a freely available online resource that provides gene, protein and sequence information for multiple Candida species, along with web-based tools for accessing, analyzing and exploring these data. The goal of CGD is to facilitate and accelerate research into Candida pathogenesis and biology. The CGD Web site is organized around Locus pages, which display information collected about individual genes. Locus pages have multiple tabs for accessing different types of information; the default Summary tab provides an overview of the gene name, aliases, phenotype and Gene Ontology curation, whereas other tabs display more in-depth information, including protein product details for coding genes, notes on changes to the sequence or structure of the gene and a comprehensive reference list. Here, in this update to previous NAR Database articles featuring CGD, we describe a new tab that we have added to the Locus page, entitled the Homology Information tab, which displays phylogeny and gene similarity information for each locus.

This article presents in detail our novel proposed methodology for detecting similarity between or among three dimensional protein structures. The innovation of our algorithm relies on the fact that during the similarity process, it has the ability to combine many attributes together and fulfill lots of preconditions, which are extensively discussed throughout the paper. Our concept is also supported by an efficient and effective indexing scheme, that provides convincing results comparing to other known methods.

We consider the following vertex-partition problem on graphs: given a simple graph G = (V, E), we want to partition G into a disjoint union of cliques by only removing a minimum number of edges. This NP-hard optimization problem is referred to as the Cluster Deletion (CD). In this paper, we propose an encoding of CD in terms of a Weighted Constraint Satisfaction Problem (WCSP), a framework which has been widely used in solving hard combinatorial problems. We compare our approach with a fixed-parameter tractability algorithm, one of the most used algorithms for solving cluster deletion. Then, we experimentally show that significant results are obtained using the WCSP encoding. We compare both solution quality and running times of these algorithms on random graphs and protein similarity graphs derived from the COG dataset.

Background Proteins play a special role in bioinformatics. The surface shape of a protein, which is an important characteristic of the protein, defines a geometric and biochemical domain where the protein interacts with other proteins. The similarity analysis among protein models has become an important topic of protein analysis, by which it can reveal the structure and the function of proteins. Results In this paper, a new protein similarity analysis method based on three-dimensional protein models is proposed. It constructs a feature matrix descriptor for each protein model combined by calculating the shape index (SI) and the related salient geometric feature (SGF), and then analyzes the protein model similarity by using this feature matrix and the extended grey relation analysis. Conclusions We compare our method to the Multi-resolution Reeb Graph (MRG) skeleton method, the L1-medial skeleton method and the local-diameter descriptor method. Experimental results show that our protein similarity analysis method is accurate and reliable while keeping the high computational efficiency.

Searching similarities among 3D protein structures deposited in macromolecular data repositories, like Protein Data Bank, is one of the time-consuming processes performed in structural bioinformatics. When performed in one-to-many or many-to-many model, the process requires increased computational resources. Moreover, exponential growth of protein structures in the Protein Data Bank causes the necessity to prepare computer systems to be able to deal with such huge volumes of data. Cloud computing provides both, theoretically infinite computational resources and a great possibility of scaling systems out and up. In this paper, we show how 3D protein structure similarity searching can be scaled out on Microsoft Azure cloud and performed by a loosely coupled, many-task computing system with local replicas of macromolecular data.

The ProBiS web server is a web server for detection of structurally similar binding sites in the PDB and for local pairwise alignment of protein structures. In this article, we present a new version of the ProBiS web server that is 10 times faster than earlier versions, due to the efficient parallelization of the ProBiS algorithm, which now allows significantly faster comparison of a protein query against the PDB and reduces the calculation time for scanning the entire PDB from hours to minutes. It also features new web services, and an improved user interface. In addition, the new web server is united with the ProBiS-Database and thus provides instant access to pre-calculated protein similarity profiles for over 29 000 non-redundant protein structures. The ProBiS web server is particularly adept at detection of secondary binding sites in proteins. It is freely available at http://probis.cmm.ki.si/old-version, and the new ProBiS web server is at http://probis.cmm.ki.si.

Protein similarity comparisons may be made on a local or global basis and may consider sequence information or differing levels of structural information. We present a local three-dimensional method that compares protein binding site surfaces in full atomic detail. The approach is based on the morphological similarity method which has been widely applied for global comparison of small molecules. We apply the method to all-by-all comparisons two sets of human protein kinases, a very diverse set of ATP-bound proteins from multiple species, and three heterogeneous benchmark protein binding site data sets. Cases of disagreement between sequence-based similarity and binding site similarity yield informative examples. Where sequence similarity is very low, high pocket similarity can reliably identify important binding motifs. Where sequence similarity is very high, significant differences in pocket similarity are related to ligand binding specificity and similarity. Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation. Proteins 2011; © 2011 Wiley-Liss, Inc.

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the current research collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins--all to all protein similarity. For the calculation of protein similarity, we use term frequency × inverse document frequency ( tf × idf ) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision. We have chosen the structural classification of proteins (SCOP) database for verification of the precision of our algorithm because it is maintained primarily by humans. The next success of this paper would be the ability to determine SCOP categories of proteins not included in the latest version of the SCOP database (v. 1.75) with nearly 100% precision.

A statistical method to predict protein pK(a) has been developed by using the 3D structure of a protein and a database of 434 experimental protein pK(a) values. Each pK(a) in the database is associated with a fingerprint that describes the chemical environment around an ionizable residue. A computational tool, MoKaBio, has been developed to identify automatically ionizable residues in a protein, generate fingerprints that describe the chemical environment around such residues, and predict pK(a) from the experimental pK(a) values in the database by using a similarity metric. The method, which retrieved the pK(a) of 429 of the 434 ionizable sites in the database correctly, was crossvalidated by leave-one-out and yielded root mean square error (RMSE) = 0.95, a result that is superior to that obtained by using the Null Model (RMSE 1.07) and other well-established protein pK(a) prediction tools. This novel approach is suitable to rationalize protein pK(a) by comparing the region around the ionizable site with similar regions whose ionizable site pK(a) is known. The pK(a) of residues that have a unique environment not represented in the training set cannot be predicted accurately, however, the method offers the advantage of being trainable to increase its predictive power.

When sequencing a new genome, its function and structure are important concerns, and inferring methods are based on protein sequence similarity methods. However, sequence groups differ in their parameters such as the number of group members and intra- and inter-class variability. A method that performs well on one group may not perform well on another group. Thus, learning similarity in a supervised manner could provide a general framework to set a similarity function to a specific sequence class. Here we describe a novel method that learns a similarity function between proteins by using a binary classifier and pairs of equivalent sequences (belonging to the same class) as positive samples, and non- equivalent sequences (belonging to different classes) as negative training samples. For sequence pair representation, we propose to use advanced techniques from fuzzy theory, including a sigmoid-type function for normalization and the class of Dombi operators that provide a more robust method. Using some additional constraints, the learned function turns out to be a valid kernel or metric function, and we present a new way of learning it, along with a new parameter-weighting technique. Using a dataset of archeal, bacterial, and eukaryotic 3-phosphoglycerate-kinase sequences (3PGK) and clusters from COG, we evaluate this equivalence learning method from a protein classification point of view. A receiver operator characteristic (ROC) analysis shows that we get a much more robust and accurate methodology for protein classification when these techniques are applied together. (See online Supplementary Material at www.liebertonline.com).

Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. The objective of a multiple network alignment is to create clusters of nodes that are evolutionarily conserved and functionally consistent across all networks. Unfortunately, the alignment methods proposed thus far do not fully meet this objective, as they are guided by pairwise scores that do not utilize the entire functional and topological information across all networks. To overcome this weakness, we propose FUSE, a multiple network aligner that utilizes all functional and topological information in all PPI networks. It works in two steps. First, it computes novel similarity scores of proteins across the PPI networks by fusing from all aligned networks both the protein wiring patterns and their sequence similarities. It does this by using Non-negative Matrix Tri-Factorization (NMTF). When we apply NMTF on the five largest and most complete PPI networks from BioGRID, we show that NMTF finds a larger number of protein pairs across the PPI networks that are functionally conserved than can be found by using protein sequence similarities alone. This demonstrates complementarity of protein sequence and their wiring patterns in the PPI networks. In the second step, FUSE uses a novel maximum weight k-partite matching approximation algorithm to find an alignment between multiple networks. We compare FUSE with the state of the art multiple network aligners and show that it produces the largest number of functionally consistent clusters that cover all aligned PPI networks. Also, FUSE is more computationally efficient than other multiple network aligners., 15 pages, 3 figures

The most popular approach to tackle the data deluge due to high throughput sequencing instruments is parallelizing applications and distributing the large datasets across cluster of computers to achieve scalability and performance. Hadoop is a generic and Shock-AWE is a customized platform for genomic data for development of such applications. In this work we compare and contrast performance of protein similarity search application based on these platforms.

In recent years we have witnessed a growth in sequencing yield, the number of samples sequenced, and as a result-the growth of publicly maintained sequence databases. The increase of data present all around has put high requirements on protein similarity search algorithms with two ever-opposite goals: how to keep the running times acceptable while maintaining a high-enough level of sensitivity. The most time consuming step of similarity search are the local alignments between query and database sequences. This step is usually performed using exact local alignment algorithms such as Smith-Waterman. Due to its quadratic time complexity, alignments of a query to the whole database are usually too slow. Therefore, the majority of the protein similarity search methods prior to doing the exact local alignment apply heuristics to reduce the number of possible candidate sequences in the database. However, there is still a need for the alignment of a query sequence to a reduced database. In this paper we present the SW#db tool and a library for fast exact similarity search. Although its running times, as a standalone tool, are comparable to the running times of BLAST, it is primarily intended to be used for exact local alignment phase in which the database of sequences has already been reduced. It uses both GPU and CPU parallelization and was 4-5 times faster than SSEARCH, 6-25 times faster than CUDASW++ and more than 20 times faster than SSW at the time of writing, using multiple queries on Swiss-prot and Uniref90 databases.

The identification of small molecules that fall within the biologically relevant subfraction of vast chemical space is of utmost importance to chemical biology and medicinal chemistry research. The prerequirement of biological relevance to be met by such molecules is fulfilled by natural product-derived compound collections. We report a structural classification of natural products (SCONP) as organizing principle for charting the known chemical space explored by nature. SCONP arranges the scaffolds of the natural products in a tree-like fashion and provides a viable analysis- and hypothesis-generating tool for the design of natural product-derived compound collections. The validity of the approach is demonstrated in the development of a previously undescribed class of selective and potent inhibitors of 11β-hydroxysteroid dehydrogenase type 1 with activity in cells guided by SCONP and protein structure similarity clustering. 11β-hydroxysteroid dehydrogenase type 1 is a target in the development of new therapies for the treatment of diabetes, the metabolic syndrome, and obesity.

Perhaps the most widely used applications of bioinformatics are tools such as psi-blast for searching sequence databases. We describe a recently developed protein database search algorithm called rankprop. rankprop relies upon a precomputed network of pairwise protein similarities. The algorithm performs a diffusion operation from a specified query protein across the protein similarity network. The resulting activation scores, assigned to each database protein, encode information about the global structure of the protein similarity network. This type of algorithm has a rich history in associationist psychology, artificial intelligence and web search. We describe the rankprop algorithm and its relatives, and we provide evidence that the algorithm successfully improves upon the rankings produced by psi-blast.

Protein similarity estimations can be achieved using reduced dimensional representations and we describe a new application for the generation of two-dimensional maps from the three-dimensional structure. The code for the dimensionality reduction is based on the concept of pseudo-random generation of two-dimensional coordinates and Monte Carlo-like acceptance criteria for the generated coordinates. A new method for calculating protein similarity is developed by introducing a distance-dependent similarity field. Similarity of two proteins is derived from similarity field indices between amino acids based on various criteria such as hydrophobicity, residue replacement factors and conformational similarity, each showing a one factor Gaussian dependence. Results on comparisons of misfolded protein models with data sets of correctly folded structures show that discrimination between correctly folded and misfolded structures is possible. Tests were carried out on five different proteins, comparing a misfolded protein structure with members of the same topology, architecture, family and domain according to the CATH classification.

The peroxin Pex24p of the yeast Yarrowia lipolytica exhibits high sequence similarity to two hypothetical proteins, Yhr150p and Ydr479p, encoded by the Saccharomyces cerevisiae genome. Like YlPex24p, both Yhr150p and Ydr479p have been shown to be integral to the peroxisomal membrane, but unlike YlPex24p, their levels of synthesis are not increased upon a shift of cells from glucose- to oleic acid–containing medium. Peroxisomes of cells deleted for either or both of the YHR150w and YDR479c genes are increased in number, exhibit extensive clustering, are smaller in area than peroxisomes of wild-type cells, and often exhibit membrane thickening between adjacent peroxisomes in a cluster. Peroxisomes isolated from cells deleted for both genes have a decreased buoyant density compared with peroxisomes isolated from wild-type cells and still exhibit clustering and peroxisomal membrane thickening. Overexpression of the genes PEX25 or VPS1, but not the gene PEX11, restored the wild-type phenotype to cells deleted for one or both of the YHR150w and YDR479c genes. Together, our data suggest a role for Yhr150p and Ydr479p, together with Pex25p and Vps1p, in regulating peroxisome number, size, and distribution in S. cerevisiae. Because of their role in peroxisome dynamics, YHR150w and YDR479c have been designated as PEX28 and PEX29, respectively, and their encoded peroxins as Pex28p and Pex29p.

Motivation: Existing algorithms for automated protein structure alignment generate contradictory results and are difficult to interpret. An algorithm which can provide a context for interpreting the alignment and uses a simple method to characterize protein structure similarity is needed. Results: We describe a heuristic for limiting the search space for structure alignment comparisons between two proteins, and an algorithm for finding minimal root-mean-squared-distance (RMSD) alignments as a function of the number of matching residue pairs within this limited search space. Our alignment algorithm uses coordinates of alpha-carbon atoms to represent each amino acid residue and requires a total computation time of O(m3n2), where m and n denote the lengths of the protein sequences. This makes our method fast enough for comparisons of moderate-size proteins (fewer than ∼800 residues) on current workstation-class computers and therefore addresses the need for a systematic analysis of multiple plausible shape similarities between two proteins using a widely accepted comparison metric. Availability: See http://www.cgl.ucsf.edu/Research/minrms Contact: tef@cgl.ucsf.edu * To whom correspondence should be addressed.

The rapid spread of the human immunodeficiency virus type 1 (HIV-1) circulating recombinant form (CRF) 01 AE throughout Asia demonstrates the dynamic nature of emerging epidemics. To further characterize the dissemination of these strains regionally, we sequenced 58 strains from Singapore and found that subtype B and CRF01 were introduced separately, by homosexual and heterosexual transmission, respectively. Protein similarity scores of the Singapore CRF01, as well as all Asian strains, demonstrated a complex distribution of scores in the V3 loop—some strains had very similar V3 loop sequences, while others were highly divergent. Furthermore, we found a strong correlation between the loss of a V3 glycosylation site and the divergent strains. This suggests that loss of this glycosylation site may make the V3 loop more susceptible to immune surveillance. The identification of a rapidly evolving population of CRF01 AE variants should be considered when designing new candidate vaccines and when evaluating breakthrough strains from current vaccine trials.

We present the assessment of the CASP4 fold recognition category. The tasks we had to execute include the splitting of multidomain targets into single domains, the classification of target domains in terms of prediction categories, the numerical evaluation of predictions, the mapping of numerical scores to quality indices, the ranking of predictors, the selection of top-performing groups, and the analysis and critical discussion of the state of the art in this field. The 125 fold recognition groups were assessed by a total score that summarizes their performance over all targets and a quality score reflecting the average quality of the submitted models. Most of the top-performing groups achieved respectable results on both scores simultaneously. Several groups submitted models that were much closer to the respective target structures than any of the known folds in the Protein Data Bank. The CASP4 assessment included the automated servers of the parallel CAFASP experiment. For the total score, the highest rank achieved by a fully automated server is 12. Two thirds of the predictors have rather low scores.

The use of a Gaussian-based representation of protein structures for evaluating protein-structure similarities and deriving three-dimensional superpositions is presented. The approach, as implemented in the program GAPS, is applied to three pairs of proteins with different topological characteristics (rich α-helix, mixed α-helix/β-strand, and rich β-strand), low sequence identities (10–30%), and recognized difficulties to define a unique optimum alignment.Validation of the GAPS superpositions is done by comparison with superpositions obtained by the TOP, GA_FIT, and ALIGN programs and those directly extracted from the FSSP database. Results suggest that a Gaussian-based methodology offers an objective means to, depending on the Gaussian-based representation, derive a consensus three-dimensional superposition when alternative superposition solutions exist.

A protein sequence database (PFDB) containing about 11,000 entries is available for Macintosh computers. The PFDB can be easily updated by importing sequences from the PIR collection through the internet. The most important feature of the database is its organization in families of closely related sequences, each family being characterized by its average dipeptide composition [Petrilli (1993), Comput. Appl. Biosci. 2, 89-93]. This allows one to perform a rapid and sensitive protein similarity search by comparing the precalculated family dipeptide composition with that of the query sequence by a linear correlation coefficient. An example of an application in which a new protein was classified by using a sequence of a fragment just 19 residues long is reported.

Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.

According to a ligand binding site, screening protein structural database may provide the lead information to accelerate drug discovery research. In this study we propose a novel approach MIIF based on the method protein folding shape code (PFSC), is applied to discover the similar binding site area of a protein related to the known drug target area of a ligand.

Screening data for phylogenetic analysis is a known Gordian knot. In this paper, GSQCT (Gene Sequence Quality Control Tool), a solution of screening gene sequence data is promoted. It is firstly to extract initial datasets using of gene annotation information; and then, to calculate the content of the uncertain character from gene sequencing for sequencing quality detection, to detect stop codons to avoid pseudogenes, to detect custom serial strings to remove contaminative sequence fragment, and to do protein similarity calculation with template protein of the object gene for homology detection and finally to decide whether to select by pre-determined threshold range, one by one. The report of the screening result is given and the multiple sequence alignment can be done to verify the homology with those verified sequences. This solution overcomes the existing gene data filtering with problems of error or ambiguous annotations and sequencing accuracy in uneven, which will lead to construct incorrect phylogenetics trees. The evaluation of the solution is introduced and shown well accuracy and effectiveness. Parallel implementation with Hadoop (Map / Reduce) for download: http://www.darwintree.cn/tools.htm

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.