Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks

By Jens Meiler, Michael Müller, Anita L. Zeidler, Felix Schmäschke. 2001

259 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Neural networks have been effectively used in a variety of prediction algorithms, including speech and facial recognition as well as the aforementioned usage in protein secondary structure prediction [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [24], [33], [34], [35], [36]”
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning (2015, methodology): “In the first iteration, only seven representative physical chemical properties of amino acid residues 34 and Position Specific Scoring Matrix (PSSM) from PSIBLAST 35 were employed to predict SS, angles, and ASA, separately.”
Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network (2014, methodology): “We also used seven representative amino-acid properties: a steric parameter (graph shape index), hydrophobicity, volume, polarizability, isoelectric point, helix probability, and sheet probability [29].”
How Structural and Physicochemical Determinants Shape Sequence Constraints in a Functional Enzyme (2015, result): “Like other programs of its kind, FoldX performs poorly at quantitatively reproducing experimental changes in stability but is considered to correctly reflect trends [34,35,39,40], which could potentially fit our goal by helping to explain the ΔΔG distributions of residues under complex constraints.”
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, methodology): “…for each amino acid (shown in Table 1 from Meiler et al. [18]); specific propensity of being endpoints of an SS segment has 11 values for each amino acid (shown in Table 1 from Duan et al. [43]); correlated contact potential has 40 values for each amino acid (shown in Table 3 from Tan et al. [44]).”