A coarse‐grained approach toNMR‐data‐assisted modeling of protein structures

Abstract

The ESCASA algorithm for analytical estimation of proton positions from coarsegrained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good-quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein-structure determination from NMR data at the all-atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse-graining is more important than high modeling accuracy.

Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions

“…Figure 1 ) by using the formulas from ( Nishikawa et al, 1974 ). The penalty function is a flat-bottom function as given by Eqs 4 – 7 ( Lubecka and Liwo, 2022 ). with where θ l , θ u , γ l , and γ u are the lower and upper boundaries on the virtual-bond angles θ and virtual-bond-dihedral angles γ , respectively (which are calculated from the boundaries on the ϕ and ψ backbone dihedral angles).…”

“…To treat ambiguous restraints, the restraint-penalties of an ambiguous set are put into a log-exp function, which has the shape of intersecting gorges and, therefore, takes the minimum value regardless of whether one only or more restraints of an ambiguous-restraint sets are satisfied (Eq. 9 ) ( Lubecka and Liwo, 2022 ). where { d } is the set of distances corresponding to a given ambiguous restraint, α is an arbitrary parameter, and V cont ( d i ; d l , d u , A ) is defined by Eq.…”

“…Figure 1) by using the formulas from (Nishikawa et al, 1974). The penalty function is a flat-bottom function as given by Eqs 4-7 (Lubecka and Liwo, 2022).…”

“…To treat ambiguous restraints, the restraint-penalties of an ambiguous set are put into a log-exp function, which has the shape of intersecting gorges and, therefore, takes the minimum value regardless of whether one only or more restraints of an ambiguous-restraint sets are satisfied (Eq. 9) (Lubecka and Liwo, 2022).…”

“…Recently, we developed a scale-consistent theory of coarse-grained force-field derivation ( Sieradzan et al, 2017 ; Liwo et al, 2020 ) and a new version of UNRES ( Liwo et al, 2019 ), which handles the β -strand and loop geometry much better, producing structures with a higher resolution, as demonstrated in the CASP13 ( Lubecka et al, 2019 ) and CASP14 ( Antoniak et al, 2021 ) community-wide experiments of the assessment of methods for protein-structure prediction ( https://predictioncenter.org ). The UNRES package has also been enhanced with the XL-MS ( Fajardo et al, 2019 ; Kogut et al, 2021 ) and NMR ( Lubecka and Liwo, 2021 , 2022 ) data-assisted-calculation capacities, which have recently been included in the server. Finally, the UNRES code has recently been heavily optimized for memory and speed ( Sieradzan et al, 2022b ) and the optimized code has been included in the server.…”