The combinatorics and extreme value statistics of protein threading

The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on “the inverse protein folding problem” laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term “protein threading.” These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

This chapter (Protein folding software) presents large pools of folded structures as determined by X-ray diffraction crystallography. The amino acid sequences for which three-dimensional structure has been ascertained are used to align the amino acid sequences in other proteins and determine their three-dimensional structures. The known structures are compiled in protein libraries such as the Proton Data Bank (PDB) and SWISS-PROT database. BLAST is a computer program that searches the data bank of proteins that have known structure and aligns the amino acid sequences in similar proteins. The efficacy of programs that determine the structure from known structures are assessed by CASP. The above type of analysis is called homology modeling. Protein threading is also described in this chapter. Ab initio modeling is used when there are few or no proteins for the comparison of protein structures. Transition state theory, similar to the transition state as used in organic/inorganic chemistry, is explained. Molecular dynamics, a powerful tool used for the investigation of protein folding, is described. Also Monte Carlo Simulations and Markov State Models are discussed in this chapter. We further explore amino acids and their involvement in α-helical and β-pleated sheets. Β-barrel proteins using hyperboloid computer programs are described. Other computer programs such as QUARK, I-TASSER, Modeller, Amber, and the Hidden Markov Model are also explained.

The emerging science of sequences: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, edited by Andreas Baxevanis and B. F. Francis Ouellette

The complete human genome has many genes of which few genes are associated with no known function but are expressed in normal as well as disease states. These proteins of unknown function have been reported in database and few structures of these sequences are also deposited in Protein Data Bank. Therefore performing computer-aided in silico analysis would enable to predict possible role using bioinformatics tools and software’s. An example of such protein structure deposited in PDB, (id: 1u5w) was selected to assign probable role in biological process. The protein sequence in fasta format was subjected to BLAST search against various protein sequence and structure databases. Analysis resulted in several similarities with other proteins/enzymes, of which Nucleoside triphosphatases were observed with reasonable similarity with the queried protein.

Homology models of proteins are of great interest for planning and analysing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases. SWISS-MODEL workspace is a web-based integrated service dedicated to protein structure homology modelling. It assists and guides the user in building protein homology models at different levels of complexity. A personal working environment is provided for each user where several modelling projects can be carried out in parallel. Protein sequence and structure databases necessary for modelling are accessible from the workspace and are updated in regular intervals. Tools for template selection, model building and structure quality evaluation can be invoked from within the workspace. Workflow and usage of the workspace are illustrated by modelling human Cyclin A1 and human Transmembrane Protease 3. The SWISS-MODEL workspace can be accessed freely at http://swissmodel.expasy.org/workspace/

BackgroundIn order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power.In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible.ResultsWe show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains.ConclusionThis study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

The protein threading problem is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches to threading are time-consuming and data-intensive. In this paper, we propose an evolution strategy for protein threading. We also developed two parallel approaches for fast threading. We have obtained at least better results than current comparable approaches, as well as significant reduction in execution time.

The protein threading problem is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches are time-consuming and data-intensive. In this paper, we propose an evolution strategy for protein threading. We also propose parallel methods for fast threading. We have obtained encouraging energy results as well as significant reduction in threading time.

Motivation: Modern sequencing technology now permits the sequencing of entire genomes, leading to thousands of new gene sequences in need of detailed annotation. It is too time consuming to predict the properties of each protein sequence manually and to organize the results of many prediction tools by hand. The prediction process must be automated so the predictions can be automatically organized, but the predictions must also be transparent. That is, the rationale for each prediction should be easily examinable by anyone that wishes to use the prediction. Results: Proteome Analyst (PA) is a web-based system for predicting the properties of each protein in a proteome. PA has three interesting features. First, it provides a single web-based tool that allows the user to select a wide range of analytic tools and automatically apply them to each protein in a proteome. In essence, PA provides one-stop automatic high-throughput analysis. Second, PA has the ability to explain its predictions to users. PA is based on established machine learning techniques, but makes every prediction transparent to its users. Third, PA allows users to easily create their own transparent custom predictors without programming. Availability: http://www.cs.ualberta.ca/~bioinfo/PA Supplementary Information: http://www.cs.ualberta.ca/~bioinfo/PA/Walkthrough http://www.cs.ualberta.ca/~bioinfo/PA/Experiments Contact: bioinfo@cs.ualberta.ca INTRODUCTION High-throughput sequencing technology has made it possible for even the smallest laboratory to sequence the genome of organelles, viruses and bacteria. Indeed there are now more than 1200 genome sequences deposited in public databases (http://www.ebi.ac.uk/genomes/) and this number is growing rapidly. Given the size and complexity of these data sets, most researchers are compelled to use automated annotation systems to identify or classify individual genes and proteins in their genomic data. A number of excellent systems have been developed over the past few years that permit automated genome-wide or proteome-wide annotation. These include GeneQuiz (Andrade et al., 1999), GeneAtlas (Kitson et al., 2002), EnsEMBL (Hubbard et al., 2002), PEDANT (Frishman et al., 2001), Genotator (Harris, 1997), Magpie (Gaasterland and Sensen, 1996) and GAIA (Overton et al., 1998). These use web-based tools to identify genes, parse data, translate sequences, search against public databases, identify domains or motifs and perform predictive analyses of protein sequences. Many of these packages provide user-customizable search schemas and graphical, hyperlinked output. The level of interpretation or inference offered by these annotation systems varies widely, with some offering only raw data (lists of homologues, calculated properties, etc.) in a consolidated format and others inferring function or ontology through detailed lexical analysis. Our PA system focuses on the task of prediction or classification. Our results show that classification/prediction can be used for many annotations. The most obvious is general-function, but we are also working on sub-cellular localization, specific function and protein-protein interaction are others we are working on. To use classification, we require an ontology or “dictionary” of class terms. Consequently, a number of controlled vocabularies or ontologies of protein function have started appearing. Among the first was the enzyme classification scheme (E.C. number) developed by the IUBMB and first employed in the ENZYME database (Bairoch, 1993). Later, Riley (1993) developed functional classification schemas for E. coli and other microbes. Variations on these functional vocabularies were added as other genomes and genome annotation systems were

One of the important goals of bioinformatics is to classify and predict the functions of proteins that have no sequence homolog of known functions. The purpose of this paper is to classify protein function by using multi-parametric feature, without sequence similarity. Firstly, we propose a method for generating novel features that present various local information of protein sequence based on positively and negatively charged residues. Then, we introduce a process of making optimal feature subset through combination of traditional and novel features extracted from protein sequence. Finally, we classify ligase enzymes by support vector machine (SVM). In experiment, only 375 out of 483 features were selected by feature selection, and the classification accuracy for 4thsub-classes in Enzyme Commission (EC) number is 98.35%. Our results demonstrate that most of novel features are valuable for specific enzyme function classification.

An update is given on the Database of Expected Fold classes (DEF) that contains a collection of fold-class predictions made from protein sequences and a mail server that provides new predictions for new sequences. To any given sequence one of 49 fold-classes is chosen to classify the structure related to the sequence with high accuracy. The updated prediction system is developed using data from the new version of the 3D-ALI database of aligned protein structures and thus is giving more reliable and more detailed predictions than the previous DEF system.

The amino acid residues on a protein surface play a key role in interaction with other molecules, determined many physical properties, and constrain the structure of the folded protein. A database of monomeric protein crystal structures was used to teach computer-simulated neural networks rules for predicting surface exposure from local sequence. These trained networks are able to correctly predict surface exposure for 72% of residues in a testing set using a binary model, (buried/exposed) and for 54% of residues using a ternary model (buried/intermediate/exposed). In the ternary model, only 11% of the exposed residues are predicted as buried and only 5% of the buried residues are predicted as exposed. Also, since the networks are able to predict exposure with a quantitative confidence estimate, it is possible to assign exposure for over half of the residues in a binary model with greater than 80% accuracy. Even more accurate predictions are obtained by making a consensus prediction of exposure for a homologous family. The effect of the local environment of an amino acid on its accessibility, though smaller than expected, is significant and accounts for the higher success rate of prediction than obtained with previously used criteria. In the absence of a three-dimensional structure, the ability to predict surface accessibility of amino acids directly from the sequence is a valuable tool in choosing sites of chemical modification or specific mutations and in studies of molecular interaction.

Protein-carbohydrate interaction is essential for biological systems, and carbohydrate-binding proteins (CBPs) are important targets when designing antiviral and anticancer drugs. Due to the high cost and difficulty associated with experimental approaches, many computational methods have been developed as complementary approaches to predict CBPs or carbohydrate-binding sites. However, most of these computational methods are not publicly available. Here, we provide a comprehensive review of related studies and demonstrate our two recently developed bioinformatics methods. The method SPOT-CBP is a template-based method for detecting CBPs based on structure through structural homology search combined with a knowledge-based scoring function. This method can yield model complex structure in addition to accurate prediction of CBPs. Furthermore, it has been observed that similarly accurate predictions can be made using structures from homology modeling, which has significantly expanded its applicability. The other method, SPRINT-CBH, is a de novo approach that predicts binding residues directly from protein sequences by using sequence information and predicted structural properties. This approach does not need structurally similar templates and thus is not limited by the current database of known protein-carbohydrate complex structures. These two complementary methods are available at https://sparks-lab.org. © 2018 by John Wiley & Sons, Inc.

Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the Protein Data Bank. Because of this disparity, methods for modeling membrane proteins are fewer and of lower quality than those for modeling soluble proteins. However, better expression, crystallization, and cryo-EM techniques have prompted a recent increase in experimental structures of membrane proteins, which can act as templates to predict the structure of closely related proteins through homology modeling. Because homology modeling relies on a structural template, it is easier and more accurate than fold recognition methods or de novo modeling, which are used when the sequence similarity between the query sequence and the sequence of related proteins in structural databases is below 25%. In homology modeling, a query sequence is mapped onto the coordinates of a single template and refined. With the increase in available templates, several templates often cover overlapping segments of the query sequence. Multi-template modeling can be used to identify the best template for local segments and join them into a single model. Here we provide a protocol for modeling membrane proteins from multiple templates in the Rosetta software suite. This approach takes advantage of several integrated frameworks, namely, RosettaScripts, RosettaCM, and RosettaMP with the membrane scoring function.

Unsteady convective diffusion in capillary chromatography