Allows protein sequence analysis. ANTHEPROT is able to interactively couple multiple alignments with secondary structure predictions. It can submit tasks on a remote server and retrieve data from a remote Web server. This tool is a complete solution for Intranet protein sequence analysis for universities, biological research institutes or biomedical companies. It permits users to integrate secondary structure predictions within multiple alignment and full interactive editing of alignments.
A protein secondary structure prediction server. JPred incorporates the Jnet algorithm in order to make more accurate predictions. In addition to protein secondary structure, JPred also makes predictions on solvent accessibility and coiled-coil regions.
A tool to predict changes in protein stability upon point mutations. The prediction model uses amino acid-atom potentials and torsion angle distribution to assess the amino acid environment of the mutation site. Additionally, the prediction model can distinguish the amino acid environment using its solvent accessibility and secondary structure specificity.
Consists of a three-dimensional visualization tool dedicated to conserved residues in discrete sequence motifs. 3MOTIF gives information about the structural environments of conserved residues, allowing researcher to focus on them for further experimentation. It can display the structural representation of residues and furnish explanation about the fact that certain positions are conserved in protein families.
Measures the distance between crosslinked residues. Jwalk uses Euclidean distance and solvent accessible surface distance (SASD) to process. SASD corresponds to the shortest distance between two residues across the surface of a protein and is more precise than the Euclidean distance for the crosslinked residues. This software suits for crosslinking mass spectrometry (XL-MS) that generates sparse structural information on proteins.
Offers a platform for determining protein structural features and tertiary structures. SCRATCH is a web application including ten modules for determining three and eight class: (1) secondary structure, (2) relative solvent accessibility, (3) domain boundaries, (4) disordered regions, (5) disulfide bridges, (6) the effect of single amino acid mutation on stability, (7) residue-residue contact maps, and (8) tertiary structures as well as contacts with other residues compared to average.
Predicts solvent accessibility of residues from protein sequences. WESA is based on five classification methods: the Bayesian statistics (BS), the multiple linear regression (MLR), the decision tree (DT), the neural network (NN) and the support vector machine (SVM) methods. It can serve to determine sites of protein hydration, which can play a role in a protein’s function. This tool can be useful to find sites of deleterious mutations.
Allows structural biologists to compare their SAXS data to the theoretical one for a model given as a PDB or PQR file. AquaSAXS takes advantage of recently developed methods, such as AquaSol, that give the equilibrium solvent density map around macromolecules, to compute an accurate SAXS/WAXS profile of a given structure and to compare it to the experimental one. AquaSAXS provides a way for the user to check and/or tune the atomic types and the corresponding parameters that are used for computing the solute and the solvent-excluded-volume contribution to the SAXS profile.
Allows prediction of secondary structure, accessible surface area and dihedral angles. SPINE-X is a secondary structure prediction method consisting of six steps of iterative prediction of secondary structure (SS), real-value residue solvent accessibility (RSA), and dihedral angles. The software can produce a distribution of three secondary structure states that is very close to the native distribution.
A regression-based method for secondary structure prediction that utilizes predicted solvent exposure of an amino acid residue as an additional attribute describing its environment. SABLE follows the 2-stage protocol of Rost and Sander, with a number of Elman-type recurrent neural networks (NNs) combined into a consensus predictor.
Allows to annotate protein sequence alignments with three-dimensional structural information. JOY serves as a post-processor to a protein structural alignment program by taking an alignment file and generating annotated alignments. Users can visualize 3D structural information in a sequence alignment to comprehend the conservation of amino acids in their specific local environments.
Consists of a modeling solution for biologics. BioLuminate is a suit that aims to address issues associated with the molecular design of biologics. The software enables protein-protein docking, protein engineering and antibody modeling. Users can also perform advanced computational analyses, including for instance helical stability/melting analysis from molecular dynamics (MD) simulations, or free energy perturbation (FEP) calculations of binding affinity and protein stability. The suite includes AggScore, which identifies aggregation hotspots.
Allows prediction of the three-dimensional structure of a protein. MUPRED uses both structural information and sequence profile information to realize this process. This tool first estimates the relative accessibility of solvents of each amino acid in the protein sought thanks to a fuzzy mean operator (FMO). The second set of features is derived from the protein position specific scoring matrix (PSSM) of a query protein.
An algorithm for protein residue-residue contact prediction. SVM-SEQ generates the predictions only based on sequence information, where secondary structures, solvent accessibility, sequence profile and sequence separations derived from the sequences are trained on contact maps by the support vector machine (SVM) technique. Based on the same number of predictions, the accuracy of the contact prediction by SVM-SEQ is comparable to the top sequence-based machine-learning methods published in the literature and in recently CASP7 experiments.
Predicts solvent accessibility of amino acids using an optimized neural network algorithm. NETASA provides accuracy values, which are comparable or better than other methods of ASA (accessible surface area) prediction. Prediction in two and three state classification systems with several thresholds are provided. This prediction method achieved the accuracy level up to 90% for training and 88% for test data sets. NETASA also includes a linear activation function and some changes in the training procedure.
Offers a platform for determining half-sphere exposure (HSE) of both alpha and beta type. SPIDER-HSE is a web platform that is based on a sequence-based method coupled to a deep neural network. The application was trained on a dataset of more than 4000 proteins. Both approaches for prediction are also integrated into the SPIDER 2 software and users can download a local standalone version.
Uses the Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. NeBcon (Neural-network and Bayes-classifier based contact prediction) is an algorithm for sequence-based protein contact prediction, built on multiple contact prediction programs, which are machine-learning, co-evolution and meta-server based. It first uses the naive Bayes classifier to calculate the posterior probability of multiple contact predictors. Neural Network is then used to train the actual contact maps against the secondary structure, solvent accessibility, Shannon entropy of multiple sequence alignments, in combination with the posterior probability scores calculated from the predictors.
Predicts position-specific solvent accessible surface areas and the relative solvent accessibility for RNAs. RNAsol is based on improved sequence profiles from the covariance models and trained with the long short-term memory (LSTM) neural networks. Some factors contribute to the performance of this method, including the improvement of sequence profiles built by aligning sequence profiles and the improvement of training by long-term memory neural networks.
Predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score. NetSurfP is composed of two neural network ensembles: the primary predicts secondary structure and have two outputs corresponding to buried or exposed; the secondary predicts the relative surface exposure of the individual amino acid residues. The tool is able to assign a reliability score to each surface accessibility prediction as an inherent part of the training process.