84 research outputs found
Accuracy of Protein-Protein Binding Sites in High-Throughput Template-Based Modeling
The accuracy of protein structures, particularly their binding sites, is essential for the success of modeling protein complexes. Computationally inexpensive methodology is required for genome-wide modeling of such structures. For systematic evaluation of potential accuracy in high-throughput modeling of binding sites, a statistical analysis of target-template sequence alignments was performed for a representative set of protein complexes. For most of the complexes, alignments containing all residues of the interface were found. The full interface alignments were obtained even in the case of poor alignments where a relatively small part of the target sequence (as low as 40%) aligned to the template sequence, with a low overall alignment identity (<30%). Although such poor overall alignments might be considered inadequate for modeling of whole proteins, the alignment of the interfaces was strong enough for docking. In the set of homology models built on these alignments, one third of those ranked 1 by a simple sequence identity criteria had RMSD<5 Å, the accuracy suitable for low-resolution template free docking. Such models corresponded to multi-domain target proteins, whereas for single-domain proteins the best models had 5 Å<RMSD<10 Å, the accuracy suitable for less sensitive structure-alignment methods. Overall, ∼50% of complexes with the interfaces modeled by high-throughput techniques had accuracy suitable for meaningful docking experiments. This percentage will grow with the increasing availability of co-crystallized protein-protein complexes
Protein Docking by the Interface Structure Similarity: How Much Structure Is Needed?
The increasing availability of co-crystallized protein-protein complexes provides an opportunity to use template-based modeling for protein-protein docking. Structure alignment techniques are useful in detection of remote target-template similarities. The size of the structure involved in the alignment is important for the success in modeling. This paper describes a systematic large-scale study to find the optimal definition/size of the interfaces for the structure alignment-based docking applications. The results showed that structural areas corresponding to the cutoff values <12 Å across the interface inadequately represent structural details of the interfaces. With the increase of the cutoff beyond 12 Å, the success rate for the benchmark set of 99 protein complexes, did not increase significantly for higher accuracy models, and decreased for lower-accuracy models. The 12 Å cutoff was optimal in our interface alignment-based docking, and a likely best choice for the large-scale (e.g., on the scale of the entire genome) applications to protein interaction networks. The results provide guidelines for the docking approaches, including high-throughput applications to modeled structures
Electronic Structure of a Hydrogenic Acceptor Impurity in Semiconductor Nano-structures
The electronic structure and binding energy of a hydrogenic acceptor impurity in 2, 1, and 0-dimensional semiconductor nano-structures (i.e. quantum well (QW), quantum well wire (QWW), and quantum dot (QD)) are studied in the framework of effective-mass envelope-function theory. The results show that (1) the energy levels monotonically decrease as the quantum confinement sizes increase; (2) the impurity energy levels decrease more slowly for QWWs and QDs as their sizes increase than for QWs; (3) the changes of the acceptor binding energies are very complex as the quantum confinement size increases; (4) the binding energies monotonically decrease as the acceptor moves away from the nano-structures’ center; (5) as the symmetry decreases, the degeneracy is lifted, and the first binding energy level in the QD splits into two branches. Our calculated results are useful for the application of semiconductor nano-structures in electronic and photoelectric devices
Coulomb gap in a model with finite charge transfer energy
The Coulomb gap in a donor-acceptor model with finite charge transfer energy
describing the electronic system on the dielectric side of the
metal-insulator transition is investigated by means of computer simulations on
two- and three-dimensional finite samples with a random distribution of equal
amounts of donor and acceptor sites. Rigorous relations reflecting the symmetry
of the model presented with respect to the exchange of donors and acceptors are
derived. In the immediate neighborhood of the Fermi energy the the
density of one-electron excitations is determined solely by
finite size effects and further away from is described by
an asymmetric power law with a non-universal exponent, depending on the
parameter .Comment: 10 pages, 6 figures, submitted to Phys. Rev.
Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives
BACKGROUND: Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. RESULTS: Here we report a new implementation of the CM method with an added set of selection rules (filters). The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR) of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. CONCLUSION: Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known
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Алгоритм предсказания структур белковых комплексов на основе генной онтологии
We propose an algorithm for comparing protein-protein complexes based on their functional properties in terms of Gene Ontology. The proposed measure of a functional similarity between complexes is combined with a structural measure to find templates for the template-based docking of protein complexes. We present the results on the modeling of protein complexes based on this algorithm.Предлагается алгоритм сравнения белок-белковых комплексов на основе их функциональных свойств в терминах генной онтологии. Мера функциональной схожести комплексов интегрируется со структурной мерой для нахождения шаблона для моделирования белковых комплексов. Приводятся результаты моделирования белковых комплексов с помощью предложенного алгоритма
Classification and Exploration of 3D Protein Domain Interactions Using Kbdock
International audienceComparing and classifying protein domain interactions according to theirthree-dimensional (3D) structures can help to understand protein structure-function and evolutionary relationships. Additionally, structural knowledge ofexisting domain–domain interactions can provide a useful way to findstructural templates with which to model the 3D structures of unsolvedprotein complexes. Here we present a straightforward guide to using the“Kbdock” protein domain structure database and its associated web site forexploring and comparing protein domain–domain interactions (DDIs) anddomain–peptide interactions (DPIs) at the Pfam domain family level. We alsobriefly explain how the Kbdock web site works, and we provide some notesand suggestions which should help to avoid some common pitfalls whenworking with 3D protein domain structures
Improved residue contact prediction using support vector machines and a large feature set
BACKGROUND: Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved. RESULTS: Here we develop a new contact map predictor (SVMcon) that uses support vector machines to predict medium- and long-range contacts. SVMcon integrates profiles, secondary structure, relative solvent accessibility, contact potentials, and other useful features. On the same test data set, SVMcon's accuracy is 4% higher than the latest version of the CMAPpro contact map predictor. SVMcon recently participated in the seventh edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7) experiment and was evaluated along with seven other contact map predictors. SVMcon was ranked as one of the top predictors, yielding the second best coverage and accuracy for contacts with sequence separation >= 12 on 13 de novo domains. CONCLUSION: We describe SVMcon, a new contact map predictor that uses SVMs and a large set of informative features. SVMcon yields good performance on medium- to long-range contact predictions and can be modularly incorporated into a structure prediction pipeline
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