The server is currently under maintenance. As a result, some services will experience unusually long delays to generate results. - The sparks lab, 2021-10-04

RNA prediction

  • SPOT-RNA: RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning (J.Singh, 2019)
  • RNAcmap: A Fully Automatic Pipeline for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis.
  • RNAsnap2: Single-sequence and Profile-based Prediction of RNA Solvent Accessibility Using Dilated Convolution Neural Network.(A.Kumar, 2020)
  • SPOT-RNA2: RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning (J.Singh, 2021)
  • SPOT-RNA-1D: RNA Backbone Torsion and Pseudotorsion Angle Prediction using Dilated Convolutional Neural Networks (J.Singh, 2021)

Protein Structure Prediction

Protein Local Structural Prediction

  • SPIDER3: Improved prediction on secondary structure and other structural properties of proteins by LSTM (R. Heffernan, 2017)
  • SPIDER2: Predicting secondary structure, local backbone angles, ASA, HSE, and Expected Errors of proteins
  • SPIDER-HSE: Predicting Half-sphere exposure (HSE, alpha and Beta) of proteins
  • SPOT-disorder: Predicting protein disorder by deep bidirectional long short-term memory recurrent neural networks (J. Hanson,2017)
  • SPOT-Contact: Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks (J. Hanson, 2018)
  • SPOT-1D: Improving prediction of protein secondary structure, backbone angles, solvent accessibility, and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks (J. Hanson, 2019)
  • SPOT-disorder2: Predicting protein disorder by an ensemble of deep IncReSenets (J. Hanson,2019)
  • SPOT-disorder-single: PAccurate single-sequence prediction of protein intrinsic disorder by an ensemble of deep recurrent and convolutional architectures (J. Hanson,2019)
  • SPOT-MoRF: Predicting molecular recognition features by transfer learning (J. Hanson,2019)

Protein Binding site prediction

  • SPRINT: Sequence-based Prediction of protein-peptide binding sites
  • SPRINT-CBH: Sequence-based Prediction of protein-carbohydrate binding sites

Protein Function Prediction

  • SPOT-peptide: Template-based prediction of peptide-binding domains and peptide-binding sites (T. Litfin, 2019)
  • SPOT-ligand: Virtual Drug Screening based on Binding Homology from protein 3D structure (Y. Yang, 2016)
  • SPOT-ligand 2: Virtual Drug Screening based on Binding Homology on expanded template library (T. Litfin, 2019)
  • SPOT-Seq-RNA: RNA Binding Protein Prediction from Sequence
  • SPOT-Struct-RNA: RNA Binding Protein Prediction from 3D structure
  • SPOT-Struct-DNA: DNA Binding Protein Domain Prediction from 3D structure
  • SPOT-CBP: Carbohydrate Binding Protein Domain Prediction (H. Zhao, 2018)

Protein annotation

  • SPRINT-Gly: Predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties (G. Taherzadeh, 2019)

Protein Structure Alignment

  • SPalign: Protein Structure Alignment
  • SPalign-NS: Non-sequential Protein Structure Alignment

Knowledge-based potential function

  • dDFIRE/DFIRE: Monomer Protein Energy Calculation

Gene Variation Analysis

  • DDIG: Detecting Disease-causing Genetic Variations due to Insertion/Deletion and Nonsense mutations
  • EASE-MM: sequence-based prediction of mutation-induced stability changes with feature-based multiple models

Protein Design

  • SPIN: Prediction of the profile of sequences compatible to a protein structure

Other services

  • DDOMAIN: Fast structure-based domain prediction using a normalized domain-domain interaction profile