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Loop modeling

About: Loop modeling is a research topic. Over the lifetime, 589 publications have been published within this topic receiving 66152 citations.


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Journal ArticleDOI
TL;DR: A set of simple and physically motivated criteria for secondary structure, programmed as a pattern‐recognition process of hydrogen‐bonded and geometrical features extracted from x‐ray coordinates is developed.
Abstract: For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats of the elementary hydrogen-bonding patterns “turn” and “bridge.” Repeating turns are “helices,” repeating bridges are “ladders,” connected ladders are “sheets.” Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain “chirality” is the torsional handedness of four consecutive Cα positions and is positive for right-handed helices and negative for ideal twisted β-sheets. Curved pieces are defined as “bends.” Solvent “exposure” is given as the number of water molecules in possible contact with a residue. The end result is a compilation of the primary structure, including SS bonds, secondary structure, and solvent exposure of 62 different globular proteins. The presentation is in linear form: strip graphs for an overall view and strip tables for the details of each of 10.925 residues. The dictionary is also available in computer-readable form for protein structure prediction work.

14,077 citations

Journal ArticleDOI
TL;DR: A comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures, which is automated and illustrated by the modelling of trypsin from two other serine proteinases.

12,386 citations

Book ChapterDOI
TL;DR: This chapter investigates the anatomy and taxonomy of protein structures, based on the results of three-dimensional X-ray crystallography of globular proteins.
Abstract: Publisher Summary This chapter investigates the anatomy and taxonomy of protein structures. A protein is a polypeptide chain made up of amino acid residues linked together in a definite sequence. Amino acids are “handed,” and naturally occurring proteins contain only L-amino acids. A simple mnemonic for that purpose is the “corncrib.” The sequence of side chains determines all that is unique about a particular protein, including its biological function and its specific three-dimensional structure. The major possible routes to knowledge of three-dimensional protein structure are prediction from the amino acid sequence and analysis of spectroscopic measurements such as circular dichroism, laser Raman spectroscopy, and nuclear magnetic resonance. The analysis and discussion of protein structure is based on the results of three-dimensional X-ray crystallography of globular proteins. The basic elements of protein structures are discussed. The most useful level at which protein structures are to be categorized is the domain, as there are many cases of multiple-domain proteins in which each separate domain resembles other entire smaller proteins. The simplest type of stable protein structure consists of polypeptide backbone wrapped more or less uniformly around the outside of a single hydrophobic core. The outline of the taxonomy is also provided in the chapter.

3,201 citations

Journal ArticleDOI
TL;DR: To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo‐electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER‐8.
Abstract: Protein structures in the Protein Data Bank provide a wealth of data about the interactions that determine the native states of proteins. Using the probability theory, we derive an atomic distance-dependent statistical potential from a sample of native structures that does not depend on any adjustable parameters (Discrete Optimized Protein Energy, or DOPE). DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures. The DOPE potential was extracted from a nonredundant set of 1472 crystallographic structures. We tested DOPE and five other scoring functions by the detection of the native state among six multiple target decoy sets, the correlation between the score and model error, and the identification of the most accurate non-native structure in the decoy set. For all decoy sets, DOPE is the best performing function in terms of all criteria, except for a tie in one criterion for one decoy set. To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo-electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER-8.

2,160 citations

Journal ArticleDOI
TL;DR: A new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment is described.
Abstract: Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures. The positions of all nonhydrogen atoms of the loop are optimized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bond angle, and improper dihedral angle terms from the CHARMM-22 force field, statistical preferences for the main-chain and side-chain dihedral angles, and statistical preferences for nonbonded atomic contacts that depend on the two atom types, their distance through space, and separation in sequence. The energy function is optimized with the method of conjugate gradients combined with molecular dynamics and simulated annealing. Typically, the predicted loop conformation corresponds to the lowest energy conformation among 500 independent optimizations. Predictions were made for 40 loops of known structure at each length from 1 to 14 residues. The accuracy of loop predictions is evaluated as a function of thoroughness of conformational sampling, loop length, and structural properties of native loops. When accuracy is measured by local superposition of the model on the native loop, 100, 90, and 30% of 4-, 8-, and 12-residue loop predictions, respectively, had <2 A RMSD error for the mainchain N, C(alpha), C, and O atoms; the average accuracies were 0.59 +/- 0.05, 1.16 +/- 0.10, and 2.61 +/- 0.16 A, respectively. To simulate real comparative modeling problems, the method was also evaluated by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment. When the RMSD distortion of the main-chain stem atoms is 2.5 A, the average loop prediction error increased by 180, 25, and 3% for 4-, 8-, and 12-residue loops, respectively. The accuracy of the lowest energy prediction for a given loop can be estimated from the structural variability among a number of low energy predictions. The relative value of the present method is gauged by (1) comparing it with one of the most successful previously described methods, and (2) describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the average accuracy of prediction is limited primarily by the accuracy of the energy function rather than by the extent of conformational sampling.

1,999 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20233
20224
20218
202014
20198
20187