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Peptide structure prediction software tools

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OPEP Files Generator / Optimized Potential for Efficient protein structure Prediction Files Generator
Generates optimized potential for efficient protein structure prediction (OPEP) files. OPEP Files Generator is a web application that combines energetic and structural accuracy and chemical specificity. It allows studying single protein properties, DNA/RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. It can be useful for systems required to be used for ART, MD, REMS, ST, MUPHY, MD/DRIVER software packages.
A computational framework, that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance. For peptides in isolation, PEP-FOLD3 returns in a few minutes useful information in the five best models for 80% of the cases. For peptides in interaction with a receptor, PEP-FOLD3 10 best clusters will contain useful information in 50% of the cases, although a more in depth analysis shows medium quality poses or better are generated for over 90% of the targets.
An energy based computational web server for an automated candidate tertiary structure prediction. Bhageerath permits predictive folding with moderate computational resources. Bhageerath accepts amino acid sequence and secondary structure information to predict 5 candidate structures for the native. It is anticipated that at least one native like structure (RMSD < 7Å without end loops) is present in the final structures. The server has been validated on 80 small globular proteins.
Predicts the tertiary structure of small peptides with sequence length varying between 7 to 35 residues. PEPstrMOD also handles peptides having various modifications like non-natural residues, terminal modifications (acetylation/amidation), cyclization (N-C, disulfide bridges), conversion of L- to D- amino acids, post translational modifications, etc. The prediction strategy is based on the realization that β-turn is an important and consistent feature of small peptides in addition to regular structures. Thus, the method uses both the regular secondary structure information predicted from PSIPRED and β-turns information predicted from BetaTurns. The structure is further refined with energy minimization and molecular dynamic simulations.
An SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates.
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