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  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2013
    In:  Nucleic Acids Research Vol. 41, No. 21 ( 2013-11), p. 9956-9966
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 41, No. 21 ( 2013-11), p. 9956-9966
    Type of Medium: Online Resource
    ISSN: 1362-4962 , 0305-1048
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2013
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 2
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 82, No. 4 ( 2014-04), p. 620-632
    Abstract: We report the first assessment of blind predictions of water positions at protein–protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community‐wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions—20 groups submitted a total of 195 models—were assessed by measuring the recall fraction of water‐mediated protein contacts. Of the 176 high‐ or medium‐quality docking models—a very good docking performance per se—only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high‐quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein–water interactions and their role in stabilizing protein complexes. Proteins 2014; 82:620–632. © 2013 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0887-3585 , 1097-0134
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 1475032-6
    SSG: 12
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  • 3
    In: PROTEOMICS, Wiley, Vol. 23, No. 17 ( 2023-09)
    Abstract: Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community‐wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non‐physiological complexes. The non‐physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein‐protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non‐physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross‐validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non‐physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.
    Type of Medium: Online Resource
    ISSN: 1615-9853 , 1615-9861
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2037674-1
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  • 4
    In: European Journal of Medicinal Chemistry, Elsevier BV, Vol. 43, No. 6 ( 2008-6), p. 1171-1179
    Type of Medium: Online Resource
    ISSN: 0223-5234
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2008
    detail.hit.zdb_id: 2005170-0
    SSG: 15,3
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  • 5
    Online Resource
    Online Resource
    Cold Spring Harbor Laboratory ; 2008
    In:  RNA Vol. 14, No. 11 ( 2008-11), p. 2274-2289
    In: RNA, Cold Spring Harbor Laboratory, Vol. 14, No. 11 ( 2008-11), p. 2274-2289
    Abstract: There is a fast growing interest in noncoding RNA transcripts. These transcripts are not translated into proteins, but play essential roles in many cellular and pathological processes. Recent efforts toward comprehension of their function has led to a substantial increase in both the number and the size of solved RNA structures. With the aim of addressing questions relating to RNA structural diversity, we examined RNA conservation at three structural levels: primary, secondary, and tertiary structure. Additionally, we developed an automated method for classifying RNA structures based on spatial (three-dimensional [3D]) similarity. Applying the method to all solved RNA structures resulted in a classified database of RNA tertiary structures (DARTS). DARTS embodies 1333 solved RNA structures classified into 94 clusters. The classification is hierarchical, reflecting the structural relationship between and within clusters. We also developed an application for searching DARTS with a new structure. The search is fast and its performance was successfully tested on all solved RNA structures since the creation of DARTS. A user-friendly interface for both the database and the search application is available online. We show intracluster and intercluster similarities in DARTS and demonstrate the usefulness of the search application. The analysis reveals the current structural repertoire of RNA and exposes common global folds and local tertiary motifs. Further study of these conserved substructures may suggest possible RNA domains and building blocks. This should be beneficial for structure prediction and for gaining insights into structure–function relationships.
    Type of Medium: Online Resource
    ISSN: 1355-8382 , 1469-9001
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2008
    detail.hit.zdb_id: 1475737-0
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2003
    In:  Bioinformatics Vol. 19, No. suppl_1 ( 2003-07-03), p. i158-i168
    In: Bioinformatics, Oxford University Press (OUP), Vol. 19, No. suppl_1 ( 2003-07-03), p. i158-i168
    Abstract: Following the hierarchical nature of protein folding, we propose a three-stage scheme for the prediction of a protein structure from its sequence. First, the sequence is cut to fragments that are each assigned a structure. Second, the assigned structures are combinatorially assembled to form the overall 3D organization. Third, highly ranked predicted arrangements are completed and refined. This work focuses on the second stage of this scheme: the combinatorial assembly. We present CombDock, a combinatorial docking algorithm. CombDock gets an ordered set of protein sub-structures and predicts the inter-contacts that define their overall organization. We reduce the combinatorial assembly to a graph-theory problem, and give a heuristic polynomial solution to this computationally hard problem. We applied CombDock to various examples of structural units of two types: protein domains and building blocks, which are relatively stable sub-structures of domains. Moreover, we tested CombDock using increasingly distorted input, where the native structural units were replaced by similarly folded units extracted from homologous proteins and, in the more difficult cases, from globally unrelated proteins. The algorithm is robust, showing low sensitivity to input distortion. This suggests that CombDock is a useful tool in protein structure prediction that may be applied to large target proteins. Supplementary information: More tables and figures are available at www.cs.tau.ac.il/~inbaryuv/combdock/ Contact: inbaryuv@tau.ac.il Keywords: structure prediction, multiple docking, hierarchical model, combinatorial assembly. *To whom correspondence should be addressed.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2003
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2011
    In:  Bioinformatics Vol. 27, No. 20 ( 2011-10-15), p. 2836-2842
    In: Bioinformatics, Oxford University Press (OUP), Vol. 27, No. 20 ( 2011-10-15), p. 2836-2842
    Abstract: Motivation: Design of protein–protein interaction (PPI) inhibitors is a key challenge in structural bioinformatics and computer-aided drug design. Peptides, which partially mimic the interface area of one of the interacting proteins, are natural candidates to form protein–peptide complexes competing with the original PPI. The prediction of such complexes is especially challenging due to the high flexibility of peptide conformations. Results: In this article, we present PepCrawler, a new tool for deriving binding peptides from protein–protein complexes and prediction of peptide–protein complexes, by performing high-resolution docking refinement and estimation of binding affinity. By using a fast path planning approach, PepCrawler rapidly generates large amounts of flexible peptide conformations, allowing backbone and side chain flexibility. A newly introduced binding energy funnel ‘steepness score’ was applied for the evaluation of the protein–peptide complexes binding affinity. PepCrawler simulations predicted high binding affinity for native protein–peptide complexes benchmark and low affinity for low-energy decoy complexes. In three cases, where wet lab data are available, the PepCrawler predictions were consistent with the data. Comparing to other state of the art flexible peptide–protein structure prediction algorithms, our algorithm is very fast, and takes only minutes to run on a single PC. Availability:  http://bioinfo3d.cs.tau.ac.il/PepCrawler/ Contact:  eladdons@tau.ac.il; wolfson@tau.ac.il
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2011
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2016
    In:  Bioinformatics Vol. 32, No. 16 ( 2016-08-15), p. 2444-2450
    In: Bioinformatics, Oxford University Press (OUP), Vol. 32, No. 16 ( 2016-08-15), p. 2444-2450
    Abstract: Motivation: A wide range of fundamental biological processes are mediated by membrane proteins. Despite their large number and importance, less than 1% of all 3D protein structures deposited in the Protein Data Bank are of membrane proteins. This is mainly due to the challenges of crystallizing such proteins or performing NMR spectroscopy analyses. All the more so, there is only a small number of membrane protein–protein complexes with known structure. Therefore, developing computational tools for docking membrane proteins is crucial. Numerous methods for docking globular proteins exist, however few have been developed especially for membrane proteins and designed to address docking within the lipid bilayer environment. Results: We present a novel algorithm, Memdock, for docking α-helical membrane proteins which takes into consideration the lipid bilayer environment for docking as well as for refining and ranking the docking candidates. We show that our algorithm improves both the docking accuracy and the candidates ranking compared to a standard protein–protein docking algorithm. Availability and Implementation:  http://bioinfo3d.cs.tau.ac.il/Memdock/ Contacts:  namih@tau.ac.il or wolfson@tau.ac.il Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 9
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2016
    In:  Bioinformatics Vol. 32, No. 15 ( 2016-08-01), p. 2289-2296
    In: Bioinformatics, Oxford University Press (OUP), Vol. 32, No. 15 ( 2016-08-01), p. 2289-2296
    Abstract: Motivation: Design of protein–protein interaction (PPI) inhibitors is a major challenge in Structural Bioinformatics. Peptides, especially short ones (5–15 amino acid long), are natural candidates for inhibition of protein–protein complexes due to several attractive features such as high structural compatibility with the protein binding site (mimicking the surface of one of the proteins), small size and the ability to form strong hotspot binding connections with the protein surface. Efficient rational peptide design is still a major challenge in computer aided drug design, due to the huge space of possible sequences, which is exponential in the length of the peptide, and the high flexibility of peptide conformations. Results: In this article we present PinaColada, a novel computational method for the design of peptide inhibitors for protein–protein interactions. We employ a version of the ant colony optimization heuristic, which is used to explore the exponential space (20n) of length n peptide sequences, in combination with our fast robotics motivated PepCrawler algorithm, which explores the conformational space for each candidate sequence. PinaColada is being run in parallel, on a DELL PowerEdge 2.8 GHZ computer with 20 cores and 256 GB memory, and takes up to 24 h to design a peptide of 5-15 amino acids length. Availability and implementation: An online server available at: http://bioinfo3d.cs.tau.ac.il/PinaColada/. Contact:  danielza@post.tau.ac.il; wolfson@tau.ac.il
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 10
    In: Journal of Molecular Biology, Elsevier BV, Vol. 435, No. 14 ( 2023-07), p. 168155-
    Type of Medium: Online Resource
    ISSN: 0022-2836
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1355192-9
    SSG: 12
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