Research Outputs

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Subject: search.filters.subject.Proteins

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Now showing 1 - 10 of 28
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    A genetic approach to reveal determinants of mitochondrial tail-anchorde protein targeting
    (Koç University, 2016) Keskin, Abdurrahman; Dunn, Cory David; 0000-0003-2393-5944; Koç University Graduate School of Sciences and Engineering; Molecular Biology and Genetics
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    A knowledge-based approach to predict protein torsion angles
    (Koç University, 2007) Tunca, Güzin; Erman, Burak; 0000-0002-2496-6059; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 179997
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    A Two-stage mathematical programming algorithm for predicting secondary structures of proteins
    (Koç University, 2003) Yılmaz, Özlem; Savaş, Selçuk; Türkay, Metin; 0000-0003-4769-6714; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 24956
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    Binding and mode coupling of proteins
    (Koç University, 2010) Gür, Mert; Erman, Burak; 0000-0002-2496-6059; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 179997
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    Biomolecular solution X-ray scattering at n2STAR beamline
    (Muğla Sıtkı Koçman Üniversitesi Fen Bilimleri Enstitüsü, 2022) Department of Molecular Biology and Genetics; N/A; Department of Molecular Biology and Genetics; Department of Molecular Biology and Genetics; Department of Molecular Biology and Genetics; Göcenler, Oktay; Yenici, Cansu Müşerref; Kahraman, Kerem; Büyükdağ, Cengizhan; Dağ, Çağdaş; Undergraduate Student; Master Student; Undergraduate Student; Undergraduate Student; Faculty Member; Department of Molecular Biology and Genetics; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); College of Sciences; Graduate School of Sciences and Engineering; College of Sciences; College of Sciences; College of Sciences; N/A; N/A; N/A; N/A; N/A
    Small angle X-ray Scattering (SAXS) is a method for determining basic structural characteristics such as the size, shape, and surface of particles. SAXS data can be used to generate low resolution models of biomolecules faster than any other conventional experimental structural biology tool. SAXS data is mostly collected in synchrotron facilities to obtain the best scattering data possible however home source SAXS devices can also generate valuable data when properly optimized. Here, we examined sample data collection and optimization at home source SAXS beamline in terms of the concentration, purity, and duration of data acquisition. We validated that high concentration, monodisperse and ultra pure protein samples obtained by size exclusion chromatography are necessary for generating viable SAXS data using a home source beamline. At least one hour is required to generate a feasible model from SAXS data, although longer data collection times do not always translate to higher resolutions. We show that with small optimizations during data collection and analysis SAXS can characterize properties such as oligomerization, molecular mass, and overall shape of particles in solution under physiological conditions. / Öz: Küçük açılı X-ışını Saçılımı (SAXS), parçacıkların boyutu, şekli ve yüzeyi gibi temel yapısal özellikleri belirlemek için kullanılan bir yöntemdir. SAXS verisi ile diğer geleneksel deneysel yapısal biyoloji araçlarından daha hızlı düşük çözünürlüklü biyomolekül modelleri hesaplanabilir. SAXS verileri, mümkün olan en iyi saçılma verilerini elde etmek için çoğunlukla senkrotron tesislerinde toplanır, ancak yerel kaynaklı SAXS cihazları da uygun şekilde optimize edildiğinde değerli veriler üretebilir. Burada, yerel kaynaklı SAXS ışın hattında numune veri toplama ve optimizasyonunu konsantrasyon, saflık ve veri toplama süresi açısından inceledik. Boyut dışlama kromatografisiyle elde edilen yüksek konsantrasyonlu, monodispers ve ultra saf protein numunelerinin, ev kaynaklı laboratuvar tipi ışın hattı kullanılarak uygulanabilir SAXS verilerinin üretilmesi için gerekli olduğunu doğruladık. Daha uzun veri toplama süresi her zaman daha yüksek çözünürlükler üretmez, ancak SAXS verilerinden uygun bir model oluşturmak için en az bir saat gereklidir. Ayrıca, hem veri toplama sırasında hem de daha sonra veri analizi sırasında küçük optimizasyonlarla SAXS, fizyolojik koşullar altında oligomerizasyon, moleküler kütle ve çözeltideki parçacıkların genel şekli gibi özellikleri belirleyebilir.
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    Character-level tagging
    (Koç University, 2016) Kuru, Onur; Oğuz, Ceyda; 0000-0003-0994-1758; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 6033
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    Comparative effects of oncogenic mutations G12C, G12V, G13D, and Q61H on local conformations and dynamics of K-Ras
    (Elsevier, 2020) Vatansever, Sezen; Gümüş, Zeynep H.; Department of Chemical and Biological Engineering; Erman, Burak; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 179997
    K-Ras is the most frequently mutated protein in human cancers. However, until very recently, its oncogenic mutants were viewed as undruggable. To develop inhibitors that directly target oncogenic K-Ras mutants, we need to understand both their mutant-specific and pan-mutant dynamics and conformations. Recently, we have investigated how the most frequently observed K-Ras mutation in cancer patients, G12D, changes its local dynamics and conformations (Vatansever et al., 2019). Here, we extend our analysis to study and compare the local effects of other frequently observed oncogenic mutations, G12C, G12V, G13D and Q61H. For this purpose, we have performed Molecular Dynamics (MD) simulations of each mutant when active (GTP-bound) and inactive (GDP-bound), analyzed their trajectories, and compared how each mutant changes local residue conformations, inter-protein distance distributions, local flexibility and residue pair correlated motions. Our results reveal that in the four active oncogenic mutants we have studied, the α2 helix moves closer to the C-terminal of the α3 helix. However, P-loop mutations cause α3 helix to move away from Loop7, and only G12 mutations change the local conformational state populations of the protein. Furthermore, the motions of coupled residues are mutant-specific: G12 mutations lead to new negative correlations between residue motions, while Q61H destroys them. Overall, our findings on the local conformational states and protein dynamics of oncogenic K-Ras mutants can provide insights for both mutant-selective and pan-mutant targeted inhibition efforts.
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    Computational and experimental investigation of Ras homodimer formation, ras-effector interactions and ras shuttling
    (Koç University, 2018) Muratcıoğlu, Serena; Keskin, Özlem; 0000-0002-4202-4049; Koç University Graduate School of Sciences and Engineering; Chemical and Biological Engineering; 26605
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    Computational design of a pentapeptide inhibitor for fibroblast growth factor receptor (FGFR3) IIIB
    (Koç University, 2011) Öztürk, Mehmet Ali; Erman, Burak; 0000-0002-2496-6059; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 179997
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    Conformation of peptides in the unfolded state: a coarse-grained model representation
    (Koç University, 2007) Engin, Özge; Erman, Burak; Sayar, Mehmet; 0000-0002-2496-6059; 0000-0003-0553-0353; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 179997; 109820