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The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling
- BIOINFORMATICS
, 2005
"... Motivation: Homology models of proteins are of great interest for planning and analyzing 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 re ..."
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Cited by 575 (5 self)
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Motivation: Homology models of proteins are of great interest for planning and analyzing 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. Results: 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
Protein structure prediction on the Web: a case study using the Phyre server,
- Nat. Protoc.
, 2009
"... Abstract Determining the structure and function of a novel protein sequence is a cornerstone of many aspects of modern biology. Over the last three decades a number of state-of-the-art computational tools for structure prediction have been developed. It is critical that the broader biological commu ..."
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Cited by 247 (10 self)
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Abstract Determining the structure and function of a novel protein sequence is a cornerstone of many aspects of modern biology. Over the last three decades a number of state-of-the-art computational tools for structure prediction have been developed. It is critical that the broader biological community are aware of such tools and, more importantly, are capable of using them and interpreting their results in an informed way. This protocol provides a guide to interpreting the output of structure prediction servers in general and details one such tool in particular, the Phyre server. Phyre is widely used by the biological community with over 150 submissions per day and provides a simple interface to what can often seem an overwhelming wealth of data.
Using the Fisher kernel method to detect remote protein homologies
- In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
, 1999
"... A new method, called the Fisher kernel method, for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a hid ..."
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Cited by 208 (4 self)
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A new method, called the Fisher kernel method, for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a hidden Markov model. The general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.
Combining pairwise sequence similarity and support vector machines for remote protein homology detection
- Proc. 6th Ann. Int. Conf. Computational Molecular Biology
, 2002
"... One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated protein is via sequence similarity with one or more prote ..."
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Cited by 205 (20 self)
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One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated protein is via sequence similarity with one or more proteins whose structure or function is already known. Toward this end, we propose a means of representing proteins using pairwise sequence similarity scores. This representation, combined with a discriminative classi � cation algorithm known as the support vector machine (SVM), provides a powerful means of detecting subtle structural and evolutionary relationships among proteins. The algorithm, called SVM-pairwise, when tested on its ability to recognize previously unseen families from the SCOP database, yields signi � cantly better performance than SVM-Fisher, pro � le HMMs, and PSI-BLAST. Key words: pairwise sequence comparison, homology, detection, support vector machines. 1.
Assignment of homology to genome sequences using a library of hidden markov models that represent all proteins of known structure
- J. Mol. Biol
, 2001
"... Protein structure prediction, to discover the fold and hence information about the probable function of the sequence of a gene about which nothing is known, is possible via homology to a sequence of ..."
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Cited by 203 (25 self)
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Protein structure prediction, to discover the fold and hence information about the probable function of the sequence of a gene about which nothing is known, is possible via homology to a sequence of
Review: Protein Secondary Structure Prediction Continues to Rise
- J. Struct. Biol
, 2001
"... f prediction accuracy? We shall see. 2001 Academic Press INTRODUCTION History. Linus Pauling correctly guessed the formation of helices and strands (14, 15) (and falsely hypothesized other structures). Three years before Pauling's guess was verified by the publications of the first X-ray stru ..."
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Cited by 180 (22 self)
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f prediction accuracy? We shall see. 2001 Academic Press INTRODUCTION History. Linus Pauling correctly guessed the formation of helices and strands (14, 15) (and falsely hypothesized other structures). Three years before Pauling's guess was verified by the publications of the first X-ray structures (16, 17), one group had already ventured to predict secondary structure from sequence (18). The first-generation prediction methods following in the 1960s and 1970s were all based on single amino acid propensities (19). The second-generation methods dominating the scene until the early 1990s used propensities for segments of 3--51 adjacent residues (19). Basically any imaginable theoretical algorithm had been applied to the problem of predicting secondary structure from sequence. However, it seemed that prediction accuracy stalled at levels slightly above 60% (percentage of residues predicted correctly in one of the three states: helix, strand, and other). The reason for this limit was the
Protein structure prediction and analysis using the Robetta server
- Nucleic Acids Res
, 2004
"... The Robetta server ..."
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Within the Twilight Zone: A Sensitive Profile-Profile Comparison Tool Based on Information Theory
- J. Mol. Biol
, 2002
"... This paper presents a novel approach to prole-prole comparison. The method compares two input proles (like those that are generated by PSI-BLAST) and assigns a similarity score to assess their statistical similarity. Our prole-prole comparison tool, which allows for gaps, can be used to detect weak ..."
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Cited by 147 (4 self)
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This paper presents a novel approach to prole-prole comparison. The method compares two input proles (like those that are generated by PSI-BLAST) and assigns a similarity score to assess their statistical similarity. Our prole-prole comparison tool, which allows for gaps, can be used to detect weak similarities between protein families. It has also been optimized to produce alignments that are in very good agreement with structural alignments. Tests show that the prole-prole alignments are indeed highly correlated with similarities between secondary structure elements and tertiary structure. Exhaustive evaluations show that our method is signicantly more sensitive in detecting distant homologies than the popular prole-based search programs PSI-BLAST and IMPALA. The relative improvement is the same order of magnitude as the improvement of PSI-BLAST relative to BLAST. Our new tool often detects similarities that fall within the twilight zone of sequence similarity
Combining phylogenetic and hidden Markov models in biosequence analysis
- J. Comput. Biol
, 2004
"... A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individ ..."
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Cited by 135 (13 self)
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A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individual sites, and hidden Markov models, which allow for changes from site to site. Besides improving the realism of ordinary phylogenetic models, they are potentially very powerful tools for inference and prediction—for gene finding, for example, or prediction of secondary structure. In this paper, we review progress on combined phylogenetic and hidden Markov models and present some extensions to previous work. Our main result is a simple and efficient method for accommodating higher-order states in the HMM, which allows for context-sensitive models of substitution— that is, models that consider the effects of neighboring bases on the pattern of substitution. We present experimental results indicating that higher-order states, autocorrelated rates, and multiple functional categories all lead to significant improvements in the fit of a combined phylogenetic and hidden Markov model, with the effect of higher-order states being particularly pronounced.
