Predicting the conformation of proteins from sequences. Progress and future progress (original) (raw)

Prediction of protein backbone conformation based on seven structure assignments

Journal of Molecular Biology, 1991

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Protein structure prediction in 2002

Current Opinion in Structural Biology, 2002

Abbreviations CASP Critical Assessment of Protein Structure Prediction HMM hidden Markov model MD molecular dynamics PDB Protein Data Bank rmsd root mean square deviation Comparative modeling The performance of the top eight comparative modeling groups at CASP4 was roughly similar [6 • ,7]. Obtaining good alignments appears to be the key element of success; loop modeling and further refinement are futile without a

Prediction of Protein Structure

Methods in Enzymology, 2004

The determination of crystal and of solution structures has been greatly rationalized over the past decade; however, it remains tedious, expensive work. In contrast, thousands of protein-encoding genes are sequenced each day. The deduced sequences of proteins provide invaluable insights into the functions of those proteins and the evolution of the organisms producing those proteins. However, much more information would be forthcoming if the structures of those proteins accompanied their sequences. This review is intended for the biologist who has no special expertise and who is not involved in the determination of protein structure. We have two goals: 1. To provide the generalist with enough background to understand the concepts, opportunities, and difficulties of protein structure prediction 2. To outline a general strategy that should allow the extraction and interpretion of structural information about a target sequence from publicly available databases, servers, and programs The (nearly) complete DNA sequences of 84 bacteria, 16 archaea, and 15 eukaryotes, including Anopheles gambiae, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Encephalitozoon cuniculi, Guillardia theta, Saccharomyces cerevisiae, Plasmodium falciparum, and Schizosaccharomyces pombe, a total of 22 billion base pairs, were available as of January 2003 from the National Institutes of Health (NIH, Bethesda, MD) or are described in the Genome News Network (Table I 1-24).

Consensus prediction of protein conformational disorder from amino acidic sequence

The open biochemistry journal, 2008

Predictions of protein conformational disorder are important in structural biology since they can allow the elimination of protein constructs, the three-dimensional structure of which cannot be determined since they are natively unfolded. Here a new procedure is presented that allows one to predict with high accuracy disordered residues on the basis of protein sequences. It makes use of twelve prediction methods and merges their results by using least-squares optimization. A statistical survey of the Protein Data Bank is also reported, in order to know how many residues can be disordered in proteins that were crystallized and the three-dimensional structure of which was determined.

Predicting protein conformation by statistical methods

Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology, 2001

The unique folded structure makes a polypeptide a functional protein. The number of known sequences is about a hundred times larger than the number of known structures and the gap is increasing rapidly. The primary goal of all structure prediction methods is to obtain structure-related information on proteins, whose structures have not been determined experimentally. Besides this goal, the development of accurate prediction methods helps to reveal principles of protein folding. Here we present a brief survey of protein structure predictions based on statistical analyses of known sequence and structure data. We discuss the background of these methods and attempt to elucidate principles, which govern structure formation of soluble and membrane proteins. ß

Addressing the Role of Conformational Diversity in Protein Structure Prediction

PLOS ONE, 2016

Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as templatebased or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.

The Evaluation of Protein Structure Prediction Results

Molecular Biotechnology, 2008

Methods for protein structure prediction are flourishing and becoming widely available to both experimentalists and computational biologists. However, how good are they? What is their range of applicability and how can we know which method is better suited for the task at hand? These are the questions that this review tries to address, by describing the worldwide Critical Assessment of techniques for protein Structure Prediction (CASP) initiative and focusing on the specific problems of assessing the quality of a protein 3D model.