Comparison of multiple amber force fields and development of improved protein backbone parameters,”

ABSTRACT The ff94 force field that is commonly associated with the Amber simulation package is one of the most widely used parameter sets for biomolecular simulation. After a decade of extensive use and testing, limitations in this force field, such as over-stabilization of a-helices, were reported by us and other researchers. This led to a number of attempts to improve these parameters, resulting in a variety of ''Amber'' force fields and significant difficulty in determining which should be used for a particular application. We show that several of these continue to suffer from inadequate balance between different secondary structure elements. In addition, the approach used in most of these studies neglected to account for the existence in Amber of two sets of backbone u/w dihedral terms. This led to parameter sets that provide unreasonable conformational preferences for glycine. We report here an effort to improve the u/w dihedral terms in the ff99 energy function. Dihedral term parameters are based on fitting the energies of multiple conformations of glycine and alanine tetrapeptides from high level ab initio quantum mechanical calculations. The new parameters for backbone dihedrals replace those in the existing ff99 force field. This parameter set, which we denote ff99SB, achieves a better balance of secondary structure elements as judged by improved distribution of backbone dihedrals for glycine and alanine with respect to PDB survey data. It also accomplishes improved agreement with published experimental data for conformational preferences of short alanine peptides and better accord with experimental NMR relaxation data of test protein systems

Ultrafast Structural Dynamics of BlsA, a Photoreceptor from the Pathogenic Bacterium Acinetobacter baumannii

Acinetobacter baumannii is an important human pathogen that can form biofilms and persist under harsh environmental conditions. Biofilm formation and virulence are modulated by blue light, which is thought to be regulated by a BLUF protein, BlsA. To understand the molecular mechanism of light sensing, we have used steady-state and ultrafast vibrational spectroscopy to compare the photoactivation mechanism of BlsA to the BLUF photosensor AppA from Rhodobacter sphaeroides. Although similar photocycles are observed, vibrational data together with homology modeling identify significant differences in the β5 strand in BlsA caused by photoactivation, which are proposed to be directly linked to downstream signaling

Unraveling the Mechanism of a LOV Domain Optogenetic Sensor:A Glutamine Lever Induces Unfolding of the Jα Helix

Light-activated protein domains provide a convenient, modular, and genetically encodable sensor for optogenetics and optobiology. Although these domains have now been deployed in numerous systems, the precise mechanism of photoactivation and the accompanying structural dynamics that modulate output domain activity remain to be fully elucidated. In the C-terminal light-oxygen-voltage (LOV) domain of plant phototropins (LOV2), blue light activation leads to formation of an adduct between a conserved Cys residue and the embedded FMN chromophore, rotation of a conserved Gln (Q513), and unfolding of a helix (Jα-helix) which is coupled to the output domain. In the present work, we focus on the allosteric pathways leading to Jα helix unfolding in Avena sativa LOV2 (AsLOV2) using an interdisciplinary approach involving molecular dynamics simulations extending to 7 μs, time-resolved infrared spectroscopy, solution NMR spectroscopy, and in-cell optogenetic experiments. In the dark state, the side chain of N414 is hydrogen bonded to the backbone N-H of Q513. The simulations predict a lever-like motion of Q513 after Cys adduct formation resulting in a loss of the interaction between the side chain of N414 and the backbone C═O of Q513, and formation of a transient hydrogen bond between the Q513 and N414 side chains. The central role of N414 in signal transduction was evaluated by site-directed mutagenesis supporting a direct link between Jα helix unfolding dynamics and the cellular function of the Zdk2-AsLOV2 optogenetic construct. Through this multifaceted approach, we show that Q513 and N414 are critical mediators of protein structural dynamics, linking the ultrafast (sub-ps) excitation of the FMN chromophore to the microsecond conformational changes that result in photoreceptor activation and biological function

A mean-field approach to the determination of peptide structures in solution.

A mean-field approach to the determination of peptide structures in solution

Protein storytelling through physics

Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics

Blinded Prediction of Protein-Ligand Binding Affinity Using Amber Thermodynamic Integration for the 2018 D3R Grand Challenge 4

In the framework of the 2018 Drug Design Data Resource (D3R) grand challenge 4, blinded predictions on relative binding free energy were performed for a set of 39 ligands of the Cathepsin S. We leveraged the GPU-accelerated thermodynamic integration (GTI) of Amber 18 to advance our computational prediction. When our entry was compared to experimental results, good correlation was observed (Kendall’s τ: 0.62, Spearman’s ρ: 0.80 and Pearson’s R: 0.82), with the highest correlation to experiment among all submissions. We designing a parallelized transformation map that placed ligands into several groups based on common alchemical substructures; TI transformations were carried out for each ligand to the relevant substructure, and between substructures. Our calculations were all conducted using the linear potential scaling scheme in Amber TI because we believe the softcore potential/dual topology approach implemented in current Amber TI is highly fault-prone. The issue was illustrated by using two examples in which typical preparation for the dual topology approach of Amber TI fails. Overall, the high accuracy of our prediction is a result of recent advances in force fields (ff14SB and GAFF), as well as rapid calculation of ensemble averages enabled by the GPU implementation of Amber. The success in a blinded prediction strongly suggests that alchemical free energy calculation in Amber is a promising tool for future commercial drug design.</p

Exploring Protocols to Build Reservoirs to Accelerate Replica Exchange MD Simulations

Replica Exchange Molecular Dynamics (REMD) is a widely used enhanced sampling method for accelerating biomolecular simulations. During the past two decades, several variants of REMD have been developed to further improve the rate of conformational sampling of REMD. One such variant, Reservoir REMD (RREMD), was shown to improve the rate of conformational sampling by around 5-20x. Despite the significant increase in sampling speed, RREMD methods have not been widely used due to the difficulties in building the reservoir and also due to the code not being available on the GPUs.In this work, we ported the AMBER RREMD code onto GPUs making it 20x faster than the CPU code. Then, we explored protocols for building Boltzmann-weighted reservoirs as well as non-Boltzmann reservoirs, and tested how each choice affects the accuracy of the resulting RREMD simulations. We show that, using the recommended protocols outlined here, RREMD simulations can accurately reproduce Boltzmann-weighted ensembles obtained by much more expensive conventional REMD simulations, with at least 15x faster convergence rates even for larger proteins (>50 amino acids) compared to conventional REMD

Dissecting the Energetics of Intrinsically Disordered Proteins via a Hybrid Experimental and Computational Approach

Intrinsically disordered proteins (IDPs) play important roles in biology, but little is known about the energetics of their inter-residue interactions. Methods that have been successfully applied to analyze the energetics of globular proteins are not applicable to the fluctuating partially ordered ensembles populated by IDPs. A combined computational experimental strategy is introduced for analyzing the energetic role of individual residues in the free state of IDPs. The approach combines experimental measurements of the binding of wild-type and mutant IDPs to their partners with alchemical free energy calculations of the structured complexes. These data allow quantitative information to be deduced about the free state via a thermodynamic cycle. The approach is validated by the analysis of the effects of mutations upon the binding free energy of the ovomucoid inhibitor third binding domain to its partners and is applied to the C-terminal domain of the measles virus nucleoprotein, a 125-residue IDP involved in the RNA transcription and replication of measles virus. The analysis reveals significant inter-residue interactions in the unbound IDP and suggests a biological role for them. The work demonstrates that advances in force fields and computational hardware have now led to the point where it is possible to develop methods, which integrate experimental and computational techniques to reveal insights that cannot be studied using either technique alone