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Offers users a wide range of options for Protein Data Bank (PDB) file conversion, modification and parameterization. PDB2PQR is a Python software package that automates many of the common tasks of preparing structures for continuum electrostatics calculations, providing a platform-independent utility for converting protein files in PDB format to PQR format. The PDB2PQR web service is driven by a modular, Python-based collection of routines, which provides considerable flexibility to the software and permits noninteractive, high-throughput usage.
AMOEBA / Atomic Multipole Optimized Energetics for Biomolecular Simulation
Treats polarization effects in various chemical and physical environments, using permanent electrostatic multipole moments through the quadrupole at each atom. AMOEBA uses an interactive atomic dipole induction scheme where the field produced by permanent multipoles and induced dipoles induces a dipole at each polarizable site, and each such induced dipole then further polarizes other atoms. AMOEBA is a part of the complete package TINKER.
Determines the oligomeric states directly from the protein sequence of a large set of fluorescent proteins (FPs) compiled from the literature. osFP employs the decision tree (DT) approach to classify FPs as being either monomeric or oligomeric. It provides six classes of sequence descriptors consisting of acid/dipeptide/tripeptide composition (AAC/ DPC/TPC), autocorrelation (AC), composition, transition and distribution (CTD), conjoint triad (Ctriad), quasi-sequence-order (QSO) and pseudo-amino acid composition (PseAAC).
PSSM-DCT / Position-specific scoring matrix—Discrete Cosine Transform
Discovers DNA-binding proteins. PSSM-DCT is based on a 100-dimension feature vector. It was used to extract sequence features from six physicochemical properties. This tool performs Discrete Wavelet Transform (DWT) and employs position specific scoring matrix (PSSM) to proceed. Its predictive performance for different parameters was tested via a five-fold cross validation. The quality of the predictor was evaluated using the Jackknife test.
VMID / Volumetric Model Inner Distances
Calculates and applies the inner distances for 3D volumetric shapes with a visibility graph. VMID is based on the visibility graph approach with a clustering technique that computes directly on the volumetric model without any surface reconstruction procedure. This software uses the inner distance for shape description via the common properties shared with known extrinsic shape descriptors. Inner distance can approximate complex shapes and avoid the problem of articulated deformation.
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