http://precedings.nature.com/collections/afp-biosapiens-2008/ Recently posted documents in AFP-Biosapiens 2008 Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2008.2239.1 One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for predicting whether a protein is involved in disease are gaining in their utility. Here, we describe an algorithm for detecting protein-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred (www.phenopred.org), is supervised: first, we map each protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encode sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then train support vector machines to detect a protein’s disease function for a number of terms in Disease Ontology (DO). We provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes and proteins can be successful even when a large number of candidate disease terms are predicted on simultaneously. http://dx.doi.org/10.1038/npre.2008.2239.1 Fri, 29 Aug 2008 12:44:48 UTC Predicting Protein-Disease Relationships Using Sequence, Physicochemical Properties, and Molecular Function Information doi:10.1038/npre.2008.2239.1 2008-08-29 Predrag Radivojac Nature Precedings 2008-08-29T12:44:48Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2214.1 The structural genomics initiatives have had as one of their aims to improve our understanding of protein function by providing representative structures for many structurally uncharacterised protein families. As suggested by the recent assessment of the Protein Structure Initiative (Structural Genomics Initiative, funded by the NIH), doubts have arisen as to whether Structural Genomics as initially planned were really beneficial to our understanding of biological issues, and in particular of protein function.A few protein domain superfamilies have been shown to account for unexpectedly large numbers of proteins encoded in fully sequenced genomes. These large superfamilies are generally very diverse, spanning a wide range of functions, both in terms of molecular activities and biological processes. Some of these superfamilies, such as the Rossmann-fold P-loop nucleotide hydrolases or the TIM-barrel glycosidases, have been the subject of extensive structural studies which in turn have shed light on how evolution of the sequence and structure properties produce functional diversity amongst homologues. Recently, the Structure-Function Linkage Database (SFLD) has been setup with the aim of helping the study of structure-function correlations in such superfamilies. Since the evolutionary success of these large superfamilies suggests biological importance, several Structural Genomics Centers have focused on providing full structural coverage for representatives of all sequence families in these superfamilies.In this work we evaluate structure/function diversity in a set of these large superfamilies and attempt to assess the quality and quantity of biological information gained from Structural Genomics. http://dx.doi.org/10.1038/npre.2008.2214.1 Fri, 22 Aug 2008 20:02:36 UTC Assessing functional novelty of PSI structures via structure-function analysis of large and diverse superfamilies doi:10.1038/npre.2008.2214.1 2008-08-22 Benoit H. Dessailly Nature Precedings 2008-08-22T20:02:36Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2209.1 Dynamics perturbation analysis (DPA) finds regions in a protein structure where proteins are “ticklish”, i.e., where interactions cause a large change in protein dynamics. Previously, such regions were shown to predict the location of native binding sites in a docking test set, but the more general applicability of DPA to the prediction of functional sites in proteins was not shown. Here we describe the results of applying an accelerated algorithm, called Fast DPA, to predict functional sites in over 50,000 SCOP domains. http://dx.doi.org/10.1038/npre.2008.2209.1 Thu, 21 Aug 2008 17:23:10 UTC Prediction of Functional Sites in SCOP Domains using Dynamics Perturbation Analysis doi:10.1038/npre.2008.2209.1 2008-08-21 Judith D. Cohn Nature Precedings 2008-08-21T17:23:10Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2207.1 Identification of protein-ligand interaction networks on a proteome scale is crucial to address a wide range of biological problems such as correlating molecular functions to physiological processes and designing safe and efficient therapeutics. We have developed a novel computational strategy to identify ligand binding profiles of proteins across gene families and applied it to predicting protein functions, elucidating molecular mechanisms of drug adverse effects, and repositioning safe pharmaceuticals to treat different diseases. http://dx.doi.org/10.1038/npre.2008.2207.1 Wed, 20 Aug 2008 20:29:38 UTC A Systematic Approach to Identifying Protein-Ligand Binding Profiles on a Proteome Scale doi:10.1038/npre.2008.2207.1 2008-08-20 Lei Xie Nature Precedings 2008-08-20T20:29:38Z Manuscript Biotechnology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2199.1 There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Below, we will briefly describe LabelHash, a new method for partial structure comparison. In partial structure comparison, the goal is to find the best geometric and chemical similarity between a set of 3D points called a motif and a subset of a set of 3D points called the target. Both the motif and targets are represented as sets of labeled 3D points. A motif is ideally composed of the functionally most-relevant residues in a binding site. The labels denote the type of residue. Motif points can have multiple labels to denote that substitutions are allowed. Any subset of the target that has labels that are compatible with the motif’s labels is called a match. The aim is to find statistically significant matches to a structural motif. Our method preprocesses a background database of targets such as a non-redundant subset of the Protein Data Bank in such a way that we can look up in constant time partial matches to a motif. Using a variant of the previously described match augmentation algorithm (1), we obtain complete matches to our motif. The nonparametric statistical model developed by (2,3) corrects for any bias introduced by our algorithm. This bias is introduced by excluding matches that do not satisfy certain geometric constraints for efficiency reasons. http://dx.doi.org/10.1038/npre.2008.2199.1 Fri, 15 Aug 2008 16:25:57 UTC LabelHash: A Flexible and Extensible Method for Matching Structural Motifs doi:10.1038/npre.2008.2199.1 2008-08-15 Mark Moll Nature Precedings 2008-08-15T16:25:57Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2193.1 We present here the Extended Similarity Group (ESG) method, which annotates query sequences with Gene Ontology (GO) terms by assigning probability to each annotation computed based on iterative PSI-BLAST searches. Conventionally sequence homology based function annotation methods, such as BLAST, retrieve function information from top hits with a significant score (E-values). In contrast, the PFP method, which we have presented previously, goes one step ahead in utilizing a PSI-BLAST result by considering very weak hits even an E-value of up to 100 and also by incorporating the functional association between GO terms (FAM matrix) computed using term co-occurrence frequencies in the UniProt database. PFP is very successful which is evidenced by the top rank in the function prediction category in CASP7 competition. Our new approach, ESG method, further improves the accuracy of PFP by essentially employing PFP in an iterative fashion. An advantage of ESG is that it is built in a rigorous statistical framework: Unlike PFP method that assigns a weighted score to each GO term, ESG assigns a probability based on weights computed using the E-value of each hit sequence on the path between the original query sequence and the current hit sequence. http://dx.doi.org/10.1038/npre.2008.2193.1 Fri, 15 Aug 2008 16:17:50 UTC ESG: Extended Similarity Group method for automated protein function prediction doi:10.1038/npre.2008.2193.1 2008-08-15 Daisuke Kihara Nature Precedings 2008-08-15T16:17:50Z Manuscript Biotechnology Ecology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2182.1 Epistatic or genetic interactions, representing the effects of mutations on the phenotypes caused by other mutations, can be very helpful for uncovering functional relationships between genes. Recently, the Epistasis Miniarray Profile (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. As part of this approach, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles. Here we present an original biclustering algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function, a goal that cannot be achieved with hierarchical clustering. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast S. cerevisiae. In addition to identifying the majority of the functional modules reported in these studies, our algorithm uncovers many recently documented and novel multi-functional relationships between genes and gene groups. http://dx.doi.org/10.1038/npre.2008.2182.1 Thu, 14 Aug 2008 21:45:30 UTC Analysis of Genetic Interaction Maps Reveals Functional Pleiotropy doi:10.1038/npre.2008.2182.1 2008-08-14 Shuye Pu Nature Precedings 2008-08-14T21:45:30Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2187.1 The explosive growth in the number of sequenced genomes has created a flood of protein sequences with unknown structure and function. A routine protocol for functional inference on an input query sequence is based on a database search for homologues. Searching a query against a non-redundant database using BLAST (or more advanced methods, e.g. PSI-BLAST) suffers from several drawbacks: (i) a local alignment often dominates the results; (ii) the reported statistical score (i.e. E-value) is often misleading; (iii) incorrect annotations may be falsely propagated. Several systematic methods are commonly used to assign sequences with functions on a genomic scale. In Pfam (1) and resources alike, statistical profiles (HMMs) are built from semi-manual multiple alignments of seed homologous sequences. The profiles are then used to scan genomic sequences for additional family members. The drawbacks of this scheme are: (i) only families with a predetermined seed are considered; (ii) the query must have a detectable sequence similarity to seed sequences; (iii) attention to internal relationships among the family members or the relations to other families is lacking; (iv) family membership is often set by pre-determined thresholds.An alternative to profile or model based methods for functional inference relies on a hierarchical clustering of the protein space, as implemented in the ProtoNet approach (2). The fundamental principle is the creation of a tree that captures evolutionary relatedness among protein families. The tree construction is fully automatic, and is based only on reported BLAST similarities among clustered sequences. The tree provides protein groupings in continuous evolutionary granularities, from closely related to distant superfamilies. Clusters in the ProtoNet tree show high correspondence with homologous sequence (i.e. Pfam and InterPro), functional (i.e. E.C. classification) and structural (i.e., SCOP) families (3). A new clustering scheme (4) has provided an extensive update to the ProtoNet process, which is now based on direct clustering of all detectable sequence similarities. Herein, we use the ProtoNet resource to develop a methodology for a consistent and safe functional inference for remote families. We illustrate the success of our approach towards clusters of poorly characterized viral proteins. Viral sequences are characterized by a rapid evolutionary rate which drives viral families to be even more remote (sequence-similarity-wise). Thus, functional inference for viral families is apparently an unsolved task. Despite this inherent difficulty, the new ProtoNet tree scaffold reliably captures weak evolutionary connections for viral families, which were previously overlooked. We take advantage of this, and propose new functional assignments for viral protein families. http://dx.doi.org/10.1038/npre.2008.2187.1 Thu, 14 Aug 2008 21:43:29 UTC Safe Functional Inference for Uncharacterized Viral Proteins doi:10.1038/npre.2008.2187.1 2008-08-14 Michal Linial Nature Precedings 2008-08-14T21:43:29Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2189.1 Predicting the function of a protein from its sequence is typically addressed using sequence-similarity. Here we propose a motif-based approach, using supervised motif extraction from protein sequences belonging to one functional family. The resulting deterministic motifs form Common Peptides (CPs) that characterize this family, allow for data mining of its proteins and facilitate further partition of the family into clusters http://dx.doi.org/10.1038/npre.2008.2189.1 Thu, 14 Aug 2008 14:14:36 UTC Data mining of protein families using common peptides doi:10.1038/npre.2008.2189.1 2008-08-14 Assaf Gottlieb Nature Precedings 2008-08-14T14:14:36Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2184.1 An application of great interest in microarray data analysis is the identification of a group of genes that show very similar patterns of expression in a data set, and are expected to represent groups of genes that perform common/similar functions, also known as functional modules. Although clustering offers a natural solution to this problem, it suffers from the limitation that it uses all the conditions to compare two genes, whereas only a subset of them may be relevant. Association analysis offers an alternative route for finding such groups of genes that may be co-expressed only over a subset of the experimental conditions used to prepare the data set. The techniques in this field attempt to find groups of data objects that contain coherent values across a set of attributes, in an exhaustive and efficient manner. In this paper, we illustrate how a generalization of the techniques in association analysis for real-valued data can be utilized to extract coherent functional modules from large microarray data sets. http://dx.doi.org/10.1038/npre.2008.2184.1 Wed, 13 Aug 2008 22:42:34 UTC Association Analysis Techniques for Discovering Functional Modules from Microarray Data doi:10.1038/npre.2008.2184.1 2008-08-13 Gaurav Pandey Nature Precedings 2008-08-13T22:42:34Z Manuscript Genetics & Genomics Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/