Structural predictions of protein–DNA binding: MELD-DNA

Abstract

Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein–DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein–DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein–DNA interactions).

When MELD Meets GaMD: Accelerating Biomolecular Landscape Exploration

“…Like data, software should be in accordance with FAIR principles (findable, accessible, interoperable, reusable). MELD has been freely accessible through GitHub for several years (), addressing various biological challenges. ,,− Our current research demonstrates its interoperability with other enhanced sampling methods, surpassing the individual efficacy of each method. Although we integrated it with GaMD in this study, the potential exists to combine MELD with alternative enhanced sampling techniques, such as metadynamics, which has already been adapted for use with multiple parallel replicas .…”

Curse and Blessing of Non‐Proteinogenic Parts in Computational Enzyme Engineering

“…Although it can generally not take non-proteogenic parts into account, [81] it can be adapted to accept ncAAs. [82] For docking nucleic acids, Flex-LzerD, [83] LightDock, [84] or the recent MELD-DNA [85] can be used. Furthermore, RoseTTAFoldNA [86] is a variant of the Baker lab's implementation of the AlphaFold framework, RoseTTAfold, [43] for the prediction of nucleic acid-protein complexes.…”

Molecular Modeling of Self-Assembling Peptides

“…13 We have previously developed MELD (Modeling Employing Limited Data) 14,15 as an enhanced sampling strategy to predict nativelike structures of proteins based on their sequences, 16 protein− protein binding, 17 protein−peptide binding, 18 protein− ligand, 19 and protein−DNA binding. 20 MELD combines the use of force fields with generic information (e.g., proteins have hydrophobic cores) that is ambiguous and noisy by design (there are many hydrophobic residues in a protein sequence, but only a few will interact in the folded state). The MELD approach should therefore be transferable to study the problem of peptide self-and coassembly.…”