Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation

Abstract

An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. In decoy discrimination tests, both Rosetta and Amber (ff14SBonlySC) energy functions performed well in scoring the native state as the lowest energy conformation in many cases, but several false minima were found in with both talaris2014 and Amber ff14SBonlySC scoring functions. The current default version of the Rosetta energy function, REF2015, which is parametrized on both small molecule and macromolecular benchmark sets to improve decoy discrimination, performs significantly better than talaris2014, highlighting the improvements made to the Rosetta scoring approach. There are no cases in Rosetta REF2015, and 8/140 cases in Amber, where a false minimum is found that is absent in the alternative landscape. In loop modeling tests, Amber ff14SBonlySC and REF2015 perform equivalently, although false minima are detected in several cases for both. The balance between dihedral, electrostatic, solvation and hydrogen bonding scores contribute to the existence of false minima. To take advantage of the semi-orthogonal nature of the Rosetta and Amber energy functions, we developed a technique that combines Amber and Rosetta conformational rankings to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation and should aid in model selection for structure evaluation and loop modeling tasks.

A molecular simulation approach towards the development of universal nanocarriers by studying the pH- and electrostatic-driven changes in the dynamic structure of albumin

“…10 However, the analysis and characterization of these protein intermediate conformational structures are very challenging and difficult. 3,11 By analyzing the protein intermediates, we can instantaneously generate an idea of the protein structure based on the resultant of conformation transformation coordinates. [12][13][14][15] BSA (bovine serum albumin) is the most common and extensively utilized protein in the eld of computational and wet lab research, and has been subjected to vigorous research for application in pharmaceuticals.…”

Protein Fold Determination by Assembling Extended Super-Secondary Structure Motifs Using Limited NMR Data

“…Rosetta relies on sequence assisted selection of fragment libraries and a physical and knowledge-based scoring function to explore the conformational space (Alford et al, 2017), and molecular dynamics rely on the physical principles encoded via the force-fields to explore the conformational space in a time-dependent manner (Brooks et al, 2009). While both Rosetta and molecular dynamics scoring functions perform on par with one another (Rubenstein et al, 2018),…”

“…. While both Rosetta and Molecular dynamics scoring functions perform on par with one another(Rubenstein et al, 2018),The DINGO approach does not use neither of these approaches but exploits the fixedconformational space encoded within the Smotifs. The DINGO approach is a genetic algorithm and does not have any random sampling routines which makes the Smotif assemblies of any target repeatable and numerically reproducible.…”

Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks

“…Guidance energy terms for design have been added to encourage certain features, such as specific amino acid compositions 39,71 , hydrogen bonding networks, or global or local net charges, and discourage others, such as repeat sequences that hinder NMR assignments, buried unsatisfied hydrogen bond donors and acceptors, or voids within the protein 175 . Further, a consensus scoring method, which utilizes the semiorthogonal nature of the Rosetta and Amber energy functions, was developed for model ranking to identify most near-native models 176 from the pool of generated decoys. This approach led to the development of a python-based tool (AmbRose) for interconversion between Rosetta and Amber models to facilitate consensus scoring.…”