Conceptual framework for performing simultaneous fold and sequence optimization in multi-scale protein modeling
Istv\'an Kolossv\'ary

TL;DR
This paper introduces a dual optimization approach for simultaneous fold and sequence prediction in multi-scale protein modeling, demonstrating improved energy minimization and revealing biases in force fields like UNRES.
Contribution
It presents a novel dual optimization framework and validates it using hidden-force Monte Carlo, showing enhanced fold and sequence predictions in off-lattice protein models.
Findings
Optimized folds have lower energy than previous models.
Sequence optimization yields more compact protein cores.
UNRES force field favors alpha helices artificially.
Abstract
We present a dual optimization concept of predicting optimal sequences as well as optimal folds of off-lattice protein models in the context of multi-scale modeling. We validate the utility of the recently introduced hidden-force Monte Carlo optimization algorithm by finding significantly lower energy folds for minimalist and detailed protein models than previously reported. Further, we also find the protein sequence that yields the lowest energy fold amongst all sequences for a given chain length and residue mixture. In particular, for protein models with a binary sequence, we show that the sequence-optimized folds form more compact cores than the lowest energy folds of the historically fixed, Fibonacci-series sequences of chain lengths of 13, 21, 34, 55, and 89. We then extend our search algorithm to use UNRES, one of the leading united-residue protein force fields. Our combined fold…
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Taxonomy
TopicsProtein Structure and Dynamics · Glycosylation and Glycoproteins Research · Enzyme Structure and Function
