Accurate contact predictions using covariation techniques and machine learning

By T. Kościółek, David T. Jones. 2015

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Here we present the results of residue–residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effective sequences, our server achieved an average top‐L/5 long‐range contact precision of 27%. MetaPSICOV method bases on a combination of classical contact prediction features, enhanced with three distinct covariation methods embedded in a two‐stage neural network predictor. Some unique features of our approach are (1) the tuning between the classical and covariation features depending on the depth of the input alignment and (2) a hybrid approach to generate deepest possible multiple‐sequence alignments by combining jackHMMer and HHblits. We discuss the CONSIP2 pipeline, our results and show that where the method underperformed, the major factor was relying on a fixed set of parameters for the initial sequence alignments and not attempting to perform domain splitting as a preprocessing step. Proteins 2016; 84(Suppl 1):145–151. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “To estimate the information in each MSA, we calculated the Neff score by clustering sequences at 62% identity, as used in a previous study 42 .”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII (2019, background): “instance.(10,11) But the key algorithmic advance appears to be the”
DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, methodology): “For instance, methods such as RaptorX [3], MetaPSICOV [11], DNCON2 [9], PconsC3 [12], and PconsC4 [2] are either available as a downloadable tool or a web server.”
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks (2017, methodology): “On the 30 free-modeling domains in the CASP11 experiment, DNCON2 achieves an average precision of 50% compared to 34.4% by MetaPSICOV and 29.7% by the best performing method CONSIP2 (Kosciolek and Jones, 2015) in CASP11, when top L/5 long-range contacts are evaluated.”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning (2021, methodology): “…or pre-training was measured. mmseqs2 was run on an Intel Skylake Gold 6248 processor with 40 threads, SSD and 377GB main memory, while protein LMs were run on a single Nvidia Quadro RTX 8000 with 48GB memory using half precision and dynamic batch size depending on sequence length (blue bar).”
Improved protein contact predictions with the MetaPSICOV2 server in CASP12 (2017, methodology): “Our assessment suggests that the new neural network architecture and domain recognition improvements in MetaPSICOV2 likely increase the predictive performance by no more than > 5%, with 13% of the CASP assessed improvement purely a consequence of the makeup of the target set and changes to the numbe”
CONFOLD2: improved contact-driven ab initio protein structure modeling (2017, methodology): “Finally, using CONFOLD2, we predicted models for the protein sequence targets in the CASP11 and CASP12 datasets with contacts predicted by the most accurate predictor in each of the CASP experiments - CONSIP2 [16] in CASP11 and Raptor-X [17] in CASP12 (see Table 1).”