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 peptide 3D structures from amino acid sequences in different environments. PepLook is based on a Boltzmann stochastic approach exploring the conformational space of peptides by an iterative calculation that uses pairs of phi/psi angles.
Computes sequence combinations of more than ten amino acids. MAPSP can be applied to all unmodified peptides up to that length as well as most bioactive peptides since terminal pyroglutamic acid and amidation were taken into account. It builds all possible combinations of peptides of the specified format. The method can be parallelized by distributing sub-tasks over a local grid network.
Designs antihypertensive (AHTs) peptides. AHTpin is a user-friendly platform providing various options to the users for predicting, designing and screening of AHT peptides. This web application enables the user to identify antihypertensive peptides from a library of peptides. It also provides the physicochemical properties of each processed peptide, which are displayed in a sorting-enabled table.
Represents procedures, and data structures such as molecules. ZAP employs opaque pointers, also called handles, and a dielectric model based on the Gaussian approach to proceed. It permits users to categorize the variation of the permittivity for a solute in a continuum solvent. This tool consists of a Poisson-Boltzmann equation (PBe) solver that did not suffer from the difficulties associated with grid-based methods.
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.
Assists user exhaustive exploration of the energy landscape of cyclic pentapeptides possibly involving chemical modifications. EGSCyP is is based on a robotic approach and a multi-level representation of the peptide. This approach can be exploited within stochastic exploration-optimization methods, such as variants of Basin Hopping (BH), and is able to provide a global picture of the conformational landscape.
Allows users to predict regular secondary structure in their peptides (e.g., H: Helix, E:Strand, C:Coil). Till date all the secondary structure prediction methods are optimized for proteins. Peptides may adopt different secondary structure when integrated in proteins. Thus it is important to develop separate method for predicting secondary structure of peptides instead of using protein secondary structure prediction methods.
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.
Identifies defensin proteins families. iDPF-PseRAAAC is a support vector machine (SVM) classifier proposed to classify the five defensin families (Insect, Invertebrate, Plant, Unclassified and Vertebrate). Compared with other existing methods, iDPF-PseRAAAC yields the highest predictive success rate. It can achieve a maximum overall accuracy (OA) of 95.10% for the defensin family, and 98.39% for the vertebrate subfamily.
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