# Protein Structure Prediction: The Next Generation

Source: [Protein Structure Prediction: The Next Generation](<https://www.sota2.com/research/paper/68fa5e8b1e209131ce9e3438>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T18:51:47.974Z

Authors: Michael C. Prentiss, Corey Hardin, Michael P. Eastwood, Chenghong Zong, Peter G. Wolynes

Published: 2006-06-12T13:37:23.000Z

## Abstract

Over the last 10-15 years a general understanding of the chemical reaction of protein folding has emerged from statistical mechanics. The lessons learned from protein folding kinetics based on energy landscape ideas have benefited protein structure prediction, in particular the development of coarse grained models. We survey results from blind structure prediction. We explore how second generation prediction energy functions can be developed by introducing information from an ensemble of previously simulated structures. This procedure relies on the assumption of a funnelled energy landscape keeping with the principle of minimal frustration. First generation simulated structures provide an improved input for associative memory energy functions in comparison to the experimental protein structures chosen on the basis of sequence alignment.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/q-bio/0606012>)

[PDF](<https://arxiv.org/pdf/q-bio/0606012.pdf>)