A Hybrid Levenberg-Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models.

Eshel Faraggi, Robert L Jernigan, Andrzej Kloczkowski

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets.Methods in molecular biology (Clifton, N.J.) · 2025
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Eshel FaraggiResearch and Information Systems, LLC, Indianapolis, IN, USA. efaraggi@gmail.com.

Robert L JerniganRoy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, USA.

Andrzej KloczkowskiBattelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.

Funding

Protein Sequence MatchingR01GM127701 · NIGMS · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L, KLOCZKOWSKI, ANDRZEJ · 2018 to 2021

$1.4M

NIGMS NIH HHS R01 GM127701

6 · The paper itself

Abstract

We have studied the ability of three types of neural networks to predict the closeness of a given protein model to the native structure associated with its sequence. We show that a partial combination of the Levenberg-Marquardt algorithm and the back-propagation algorithm produced the best results, giving the lowest error and largest Pearson correlation coefficient. We also find, as previous studies, that adding associative memory to a neural network improves its performance. Additionally, we find that the hybrid method we propose was the most robust in the sense that other configurations of it experienced less decline in comparison to the other methods. We find that the hybrid networks also undergo more fluctuations on the path to convergence. We propose that these fluctuations allow for better sampling. Overall we find it may be beneficial to treat different parts of a neural network with varied computational approaches during optimization.

Indexed as

Neural Networks, ComputerAlgorithmsProteinsProteinsAssociative memoryLevenberg–Marquardt algorithmModel quality assessmentNeural networkProtein structure

Identifiers

PMID32804373

DOI10.1007/978-1-0716-0826-5_15

PMCPMC7666373

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.