Porter: a new, accurate server for protein secondary structure prediction

By Gianluca Pollastri, Aoife McLysaght. Bioinform., 2005

484 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/712e389056d04cbc2966f5d5175f05c260a7f9a7

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In Materials Chemistry

In Molecular Biology

Citations in the corpus

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014, methodology): “For testing purposes, we use predictions from in-house servers [42,44,45] to predict secondary structure, solvent accessibility and contact density, respectively.”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Combining such machine learning technology with protein sequence profiles based on homology produced a major breakthrough in secondary structure prediction, raising accuracy to 70-79 percent [16], [18], [19], [20], [21].”
MUFOLD-SS: New Deep Inception-Inside-Inception Networks for Protein Secondary Structure Prediction (2018, methodology): “The previous protein secondary structure methods can be divided into two main categories: template-based methods(1) using known protein structures as templates or template-free methods.(7) Although template-based methods often perform better than template-free methods, in the Critical Assessment of ”
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models (2006, methodology): “Among the current best methods that use evolutionary information (multiple alignments, PSI-BLAST profiles), one can mention PSIPRED [1], Porter [23], SSpro [24], APSSP2 [2], SVMpsi [9], PHDpsi [25], JPRED2 [4] and PROF [26].”
iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, methodology): “Similarly, the predictors of the residue-level descriptors include methods for prediction of the secondary structure (Jones 1999; McGuffin et al. 2000; Rost 2001, 2005, 2008; Rost et al. 2004; Pollastri and McLysaght 2005; Ofer and Yaoqi 2007; Montgomerie et al. 2008; Kurgan 2008; Ding et al. 2009”
Local Protein Structures (2007, background): “They differ in the parameters used to describe the protein backbone like Cα coordinates, Cα distances, α or dihedral angles and in the methods used to H A L author m anuscript inserm -00175058, version 1 define them such as k-means [286], empirical function, Kohonen Maps [287, 288], artificial neura”
Machine Learning Methods for Protein Structure Prediction (2008, methodology): “Large ensembles of hundreds of neural networks have also been used [55].”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, methodology): “We include web servers for the two homology-based methods, SSpro [17] and PROTEUS [31], the well-performing ab-initio PSIPRED [21] and PORTER [24], for which the web server includes homology modeling.”