Results 201 to 210 of about 214,637 (252)

A spectral framework for measuring diversity in multiple sequence alignments. [PDF]

PLoS Comput Biol
Opuu V.
europepmc   +1 more source

Aptamers without SELEX: <i>De novo</i> design from protein structure in hours. [PDF]

Mol Ther Nucleic Acids
Dolgusheva VA, Leibin IV, Kudryashova NO, Mukanov AR, Kuznetsov II, Manasyan AL, Pavlyuk GA, Mishkorez IV, Keremet AM, Podlesnyi PR, Kalinkin AN, Avdeev KS, Kuznetsov MD, Parfenov NO, Gromov SM, Ufaev D, Nasaev SS.   +16 more
europepmc   +1 more source

ProNA3D: Distance-Based Analysis of Nucleic Acid-Containing Interfaces. [PDF]

Comput Struct Biotechnol J
Genz LR, Topf M.
europepmc   +1 more source

HighFold4: extending AlphaFold3 to accurate cyclic peptide conformation prediction via custom chemical connectivity. [PDF]

Brief Bioinform
Zhang C, Wang W, Zhu R, Cao S, Xu Y, Zhu N, Wu Y, Guo J, Duan H.   +8 more
europepmc   +1 more source

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Protein Secondary Structure Prediction

Current Protocols in Protein Science, 1995
AbstractThis unit describes procedures developed for predicting protein structure from the amino acid sequence. The first of the four sections is an overview and brief history of structure prediction schemes. The second section describes four distinct prediction schemes, with emphasis on their differences.
S R, Krystek, W J, Metzler, J, Novotny
openaire   +5 more sources

Redefining the Goals of Protein Secondary Structure Prediction

Journal of Molecular Biology, 1994
Secondary structure prediction recently has surpassed the 70% level of average accuracy, evaluated on the single residue states helix, strand and loop (Q3). But the ultimate goal is reliable prediction of tertiary (three-dimensional, 3D) structure, not 100% single residue accuracy for secondary structure.
Chris Sander, Reinhard Schneider, Burkhard Rost   +2 more
exaly   +4 more sources

Protein Secondary Structure Prediction with SPARROW

Journal of Chemical Information and Modeling, 2012
A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used.
Francesco Bettella, Dawid Rasinski, Ernst-Walter Knapp   +2 more
openaire   +2 more sources

Improving Protein Secondary-Structure Prediction by Predicting Ends of Secondary-Structure Segments

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005
Motivated by known preferences for certain amino acids in positions around a-helices, we developed neural network-based predictors of both N and C a-helix ends, which achieved about 88% accuracy. We applied a similar approach for predicting the ends of three types of secondary structure segments.
Uros Midic, A. Keith Dunker, Zoran Obradovic   +2 more
openaire   +2 more sources