Srinivasa M. Gopal

PRIMO/PRIMONA: A coarse‐grained model for proteins and nucleic acids that preserves near‐atomistic accuracy

The new coarse graining model PRIMO/PRIMONA for proteins and nucleic acids is proposed. This model combines one to several heavy atoms into coarse‐grained sites that are chosen to allow an analytical, high‐resolution reconstruction of all‐atom models based on molecular bonding geometry constraints. The accuracy of proposed reconstruction method in terms of structure and energetics is tested and compared with other popular reconstruction methods for a variety of protein and nucleic acid test sets. Proteins 2010.

Conformational sampling of peptides in the presence of protein crowders from AA/CG-multiscale simulations.

Macromolecular crowding is recognized as an important factor influencing folding and conformational dynamics of proteins and nucleic acids. Previous views of crowding have focused on the mostly entropic volume exclusion effect of crowding, but recent studies are indicating the importance of enthalpic effects, in particular, changes in electrostatic interactions due to crowding. Here, temperature replica exchange molecular dynamics simulations of trp-cage and melittin in the presence of explicit protein crowders are presented to further examine the effect of protein crowders on peptide dynamics. The simulations involve a three-component multiscale modeling scheme where the peptides are represented at an atomistic level, the crowder proteins at a coarse-grained level, and the surrounding aqueous solvent as implicit solvent. This scheme optimally balances a physically realistic description for the peptide with computational efficiency. The multiscale simulations were compared with simulations of the same peptides in different dielectric environments with dielectric constants ranging from 5 to 80. It is found that the sampling in the presence of the crowders resembles sampling with reduced dielectric constants between 10 and 40. Furthermore, diverse conformational ensembles are generated in the presence of crowders including partially unfolded states for trp-cage. These findings emphasize the importance of enthalpic interactions over volume exclusion effects in describing the effects of cellular crowding.

A peptidic unconjugated GRP78/BiP ligand modulates the unfolded protein response and induces prostate cancer cell death.

The molecular chaperone GRP78/BiP is a key regulator of protein folding in the endoplasmic reticulum, and it plays a pivotal role in cancer cell survival and chemoresistance. Inhibition of its function has therefore been an important strategy for inhibiting tumor cell growth in cancer therapy. Previous efforts to achieve this goal have used peptides that bind to GRP78/BiP conjugated to pro-drugs or cell-death-inducing sequences. Here, we describe a peptide that induces prostate tumor cell death without the need of any conjugating sequences. This peptide is a sequence derived from the cochaperone Bag-1. We have shown that this sequence interacts with and inhibits the refolding activity of GRP78/BiP. Furthermore, we have demonstrated that it modulates the unfolded protein response in ER stress resulting in PARP and caspase-4 cleavage. Prostate cancer cells stably expressing this peptide showed reduced growth and increased apoptosis in in vivo xenograft tumor models. Amino acid substitutions that destroyed binding of the Bag-1 peptide to GRP78/BiP or downregulation of the expression of GRP78 compromised the inhibitory effect of this peptide. This sequence therefore represents a candidate lead peptide for anti-tumor therapy.

Transferring the PRIMO Coarse-Grained Force Field to the Membrane Environment: Simulations of Membrane Proteins and Helix–Helix Association

An extension of the recently developed PRIMO coarse-grained force field to membrane environments, PRIMO-M, is described. The membrane environment is modeled with the heterogeneous dielectric generalized Born (HDGB) methodology that simply replaces the standard generalized Born model in PRIMO without further parametrization. The resulting model was validated by comparing amino acid insertion free energy profiles and application in molecular dynamics simulations of membrane proteins and membrane-interacting peptides. Membrane proteins with 148–661 amino acids show stable root-mean-squared-deviations (RMSD) between 2 and 4 Å for most systems. Transmembrane helical peptides maintain helical shape and exhibit tilt angles in good agreement with experimental or other simulation data. The association of two glycophorin A (GpA) helices was simulated using replica exchange molecular dynamics simulations yielding the correct dimer structure with a crossing angle in agreement with previous studies. Finally, conformational sampling of the influenza fusion peptide also generates structures in agreement with previous studies. Overall, these findings suggest that PRIMO-M can be used to study membrane bound peptides and proteins and validates the transferable nature of the PRIMO coarse-grained force field.

All-atom de novo protein folding with a scalable evolutionary algorithm.

The search for efficient and predictive methods to describe the protein folding process at the all‐atom level remains an important grand‐computational challenge. The development of multi‐teraflop architectures, such as the IBM BlueGene used in this study, has been motivated in part by the large computational requirements of such studies. Here we report the predictive all‐atom folding of the forty‐amino acid HIV accessory protein using an evolutionary stochastic optimization technique. We implemented the optimization method as a master‐client model on an IBM BlueGene, where the algorithm scales near perfectly from 64 to 4096 processors in virtual processor mode. Starting from a completely extended conformation, we optimize a population of 64 conformations of the protein in our all‐atom free‐energy model PFF01. Using 2048 processors the algorithm predictively folds the protein to a near‐native conformation with an RMS deviation of 3.43 Å in <24 h.

On Using Atomistic Solvent Layers in Hybrid All-Atom/Coarse-Grained Molecular Dynamics Simulations

Hybrid all-atom/coarse-grained (AA-CG) simulations in which AA solutes are embedded in a CG environment can provide a significant computational speed-up over conventional fully atomistic simulations and thus alleviate the current length and time scale limitations of molecular dynamics (MD) simulations of large biomolecular systems. On one hand, coarse graining the solvent is particularly appealing, since it typically constitutes the largest part of the simulation system and thus dominates computational cost. On the other hand, retaining atomic-level solvent layers around the solute is desirable for a realistic description of hydrogen bonds and other local solvation effects. Here, we devise and systematically validate fixed resolution AA-CG schemes, both with and without atomistic water layers. To quantify the accuracy and diagnose possible pitfalls, Gibbs free energies of solvation of amino acid side chain analogues were calculated, and the influence of the nature of the CG solvent surrounding (polarizable vs nonpolarizable CG water) and the size of the AA solvent region was investigated. We show that distance restraints to keep the AA solvent around the solute lead to too high of a density in the inner shell. Together with a long-ranged effect due to orientational ordering of water molecules at the AA-CG boundary, this affects solvation free energies. Shifting the onset of the distance restraints slightly away from the central solute significantly improves solvation free energies, down to mean unsigned errors with respect to experiment of 2.3 and 2.6 kJ/mol for the polarizable and nonpolarizable CG water surrounding, respectively. The speed-up of the nonpolarizable model renders it computationally more attractive. The present work thus highlights challenges, and outlines possible solutions, involved with modeling the boundary between different levels of resolution in hybrid AA-CG simulations.

Template-free protein structure prediction and quality assessment with an all-atom free-energy model

Biophysical forcefields have contributed less than originally anticipated to recent progress in protein structure prediction. Here, we have investigated the selectivity of a recently developed all‐atom free‐energy forcefield for protein structure prediction and quality assessment (QA). Using a heuristic method, but excluding homology, we generated decoy‐sets for all targets of the CASP7 protein structure prediction assessment with <150 amino acids. The decoys in each set were then ranked by energy in short relaxation simulations and the best low‐energy cluster was submitted as a prediction. For four of nine template‐free targets, this approach generated high‐ranking predictions within the top 10 models submitted in CASP7 for the respective targets. For these targets, our de‐novo predictions had an average GDT_S score of 42.81, significantly above the average of all groups. The refinement protocol has difficulty for oligomeric targets and when no near‐native decoys are generated in the decoy library. For targets with high‐quality decoy sets the refinement approach was highly selective. Motivated by this observation, we rescored all server submissions up to 200 amino acids using a similar refinement protocol, but using no clustering, in a QA exercise. We found an excellent correlation between the best server models and those with the lowest energy in the forcefield. The free‐energy refinement protocol may thus be an efficient tool for relative QA and protein structure prediction. Proteins 2009.

Molecular Dynamics Trajectory Compression with a Coarse-Grained Model

Molecular dynamics trajectories are very data intensive thereby limiting sharing and archival of such data. One possible solution is compression of trajectory data. Here, trajectory compression based on conversion to the coarse-grained model PRIMO is proposed. The compressed data are about one third of the original data and fast decompression is possible with an analytical reconstruction procedure from PRIMO to all-atom representations. This protocol largely preserves structural features and to a more limited extent also energetic features of the original trajectory.

Conformational Sampling in Structure Prediction and Refinement with Atomistic and Coarse-Grained Models

The performance of models at different resolutions is compared in the context of an iterative protein structure refinement protocol. The models considered here consist of an all-atom model described with the CHARMM22 force field in combination with a distance-dependent dielectric implicit solvent approximation, a united-atom model described by the CHARMM19 force field in combination with the effective energy function 1 (EEF1) solvent model, the new intermediate coarse-grained model PRotein Intermediate MOdel (PRIMO), both with Generalized Born and distance-dependent dielectric solvent models, and the lattice-based coarse-grained Side CHain Only (SICHO) model. It is found that the CHARMM19 and SICHO models lead to initially rapid refinement for a set consisting of 11 targets from past critical assessment of protein structure prediction (CASP) competitions, but they are eventually outperformed by the CHARMM22 and PRIMO models in consistently reaching near-native conformations over the course of 100 refinement cycles.

All-Atom Protein Folding with Free-Energy Forcefields

Publisher Summary Proteins are the workhorses of all cellular life. They constitute the building blocks and the machinery of all cells. DNA carries the genetic information which encodes the production of protein molecules. The triplet genetic code by which the DNA sequence determines the amino acid sequence of polypeptide chains is well understood. However, unfolded polypeptide chains lack most of the properties needed for their biological function. There are large number of related questions, for instance, regarding the interactions of a given protein with a large variety of other proteins, where theoretical methods could also contribute to our understanding of biological function. Protein–protein interactions govern the cell signaling processes and are very important for the assembly of large protein structures in the cell. The ultimate, very long-range goal of protein structure theory would be the development of methods to design proteins for a specific function. This would be very helpful for medical purposes and technological applications in nanobiology, but will require an understanding of various factors that influence the folding of the polypeptide and their sequence determinants.