Comment on 'Valid molecular dynamics simulations of human hemoglobin require a surprisingly large box size'.

A recent molecular dynamics investigation into the stability of hemoglobin concluded that the unliganded protein is only stable in the T state when a solvent box is used in the simulations that is ten times larger than what is usually employed (El Hage et al., 2018). Here, we express three main concerns about that study. In addition, we find that with an order of magnitude more statistics, the reported box size dependence is not reproducible. Overall, no significant effects on the kinetics or thermodynamics of conformational transitions were observed

Predicting kinase inhibitor resistance: Physics-based and data-driven approaches.

Resistance to small molecule drugs often emerges in cancer cells, viruses, and bacteria as a result of the evolutionary pressure exerted by the therapy. Protein mutations that directly impair drug binding are frequently involved in resistance, and the ability to anticipate these mutations would be beneficial in drug development and clinical practice. Here, we evaluate the ability of three distinct computational methods to predict ligand binding affinity changes upon protein mutation for the cancer target Abl kinase. These structure-based approaches rely on first-principle statistical mechanics, mixed physics- and knowledge-based potentials, and machine learning, and were able to estimate binding affinity changes and identify resistant mutations with remarkable accuracy. We expect that these complementary approaches will enable the routine prediction of resistance-causing mutations in a variety of other target proteins

On the importance of statistics in molecular simulations for thermodynamics, kinetics and simulation box size

Computational simulations, akin to wetlab experimentation, are subject to statistical fluctuations. Assessing the magnitude of these fluctuations, that is, assigning uncertainties to the computed results, is of critical importance to drawing statistically reliable conclusions. Here, we use a simulation box size as an independent variable, to demonstrate how crucial it is to gather sufficient amounts of data before drawing any conclusions about the potential thermodynamic and kinetic effects. In various systems, ranging from solvation free energies to protein conformational transition rates, we showcase how the proposed simulation box size effect disappears with increased sampling. This indicates that, if at all, the simulation box size only minimally affects both the thermodynamics and kinetics of the type of biomolecular systems presented in this work

One plus one makes three: Triangular coupling of correlated amino acid mutations

Correlated mutations have played a pivotal role in the recent success in protein fold prediction. Understanding nonadditive effects of mutations is crucial for altering protein structure, as mutations of multiple residues may change protein stability or binding affinity in a manner unforeseen by the investigation of single mutants. While the couplings between amino acids can be inferred from homologous protein sequences, the physical mechanisms underlying these correlations remain elusive. In this work we demonstrate that calculations based on the first-principles of statistical mechanics are capable of capturing the effects of nonadditivities in protein mutations. The identified thermodynamic couplings cover the short-range as well as previously unknown long-range correlations. We further explore a set of mutations in staphyloccocal nuclease to unravel an intricate interaction pathway underlying the correlations between amino acid mutations

Improving pK_a Predictions with Reparameterized Force Fields and Free Energy Calculations

Given the growing interest in designing targeted covalent inhibitors, methods for rapidly and accurately probing pKas─and, by extension, the reactivities─of target cysteines are highly desirable. Complementary to cysteine, histidine is similarly relevant due to its frequent presence in protein active sites and its unique ability to exist in two tautomeric states. Here, we demonstrate that nonequilibrium free energy calculations can accurately determine the pKa values of both residues, often outperforming conventional predictors. Importantly, we find that (1) increasing the van der Waals radius of cysteine’s sulfur atom, (2) modifying the backbone charges of histidine, and (3) introducing effective polarization by downscaling the side chain partial charges of both residues can all significantly improve pKa prediction accuracy. Using the modified CHARMM36m force field on the full dataset reduces the prediction error from 2.12 ± 0.27 pK to 1.28 ± 0.15 pK and increases the correlation with experiment from 0.25 ± 0.09 to 0.58 ± 0.08. Similarly, using the modified Amber14SB force field decreases the error from 3.21 ± 0.29 pK to 1.69 ± 0.23 pK and improves the correlation from 0.15 ± 0.10 to 0.36 ± 0.10

Challenges encountered applying equilibrium and nonequilibrium binding free energy calculations

Binding free energy calculations have become increasingly valuable to drive decision making in drug discovery projects. However, among other issues, inadequate sampling can reduce accuracy, limiting the value of the technique. In this paper, we apply absolute binding free energy calculations to ligands binding to T4 lysozyme L99A and HSP90 using equilibrium and nonequilibrium approaches. We highlight sampling problems encountered in these systems, such as slow side chain rearrangements and slow changes of water placement upon ligand binding. These same types of challenges are also likely to show up in other protein–ligand systems, and we propose some strategies to diagnose and test for such problems in alchemical free energy calculations. We also explore similarities and differences in how the equilibrium and the nonequilibrium approaches handle these problems. Our results show the large amount of work still to be done to make free energy calculations robust and reliable and provide insight for future research in this area

Resolving the atomistic modes of anle138b inhibitory action on peptide oligomer formation.

The di-phenyl-pyrazole compound anle138b is a known inhibitor of oligomeric aggregate formation in vitro and in vivo. Therefore, anle138b is considered a promising drug candidate to beneficially interfere with neurodegenerative processes causing devastating pathologies in humans. The atomistic details of the aggregation inhibition mechanism, however, are to date unknown since the ensemble of small nonfibrillar aggregates is structurally heterogeneous and inaccessible to direct structural characterization. Here, we set out to elucidate anle138b's mode of action using all-atom molecular dynamics simulations on the multi-microsecond timescale. By comparing simulations of dimeric to tetrameric aggregates from fragments of four amyloidogenic proteins (Aβ, hTau40, hIAPP and Sup35N) in the presence and absence of anle138b, we show that the compound reduces the overall number of intermolecular hydrogen bonds, disfavors the sampling of the aggregated state and remodels the conformational distributions within the small oligomeric peptide aggregates. Most notably, anle138b preferentially interacts with the disordered structure ensemble via its pyrazole moiety, thereby effectively blocking interpeptide main chain interactions and impeding the spontaneous formation of ordered β-sheet structures, in particular those with out-of-register anti-parallel β-strands. The structurally very similar compound anle234b was previously identified as inactive by in vitro experiments. Here, we show that anle234b has no significant effect on the aggregation process in terms of reducing the β-structure content. Moreover, we demonstrate that the hydrogen bonding capabilities are auto-inhibited due to steric effects imposed by the molecular geometry of anle234b and thereby indirectly confirm the proposed inhibitory mechanism of anle138b. We anticipate that the prominent binding of anle138b to partially disordered and dynamical aggregate structures is a generic basis for anle138b's ability to suppress toxic oligomer formation in a wide range of amyloidogenic peptides and proteins

Accurately Predicting Protein p<i>K</i> a Values Using Nonequilibrium Alchemy

The stability, solubility, and function of a protein depend on both its net charge and the protonation states of its individual residues. pKa is a measure of the tendency for a given residue to (de)protonate at a specific pH. Although pKa values can be resolved experimentally, theory and computation provide a compelling alternative. To this end, we assess the applicability of a nonequilibrium (NEQ) alchemical free energy method to the problem of pKa prediction. On a data set of 144 residues that span 13 proteins, we report an average unsigned error of 0.77 ± 0.09, 0.69 ± 0.09, and 0.52 ± 0.04 pK for aspartate, glutamate, and lysine, respectively. This is comparable to current state-of-the-art predictors and the accuracy recently reached using free energy perturbation methods (e.g., FEP+). Moreover, we demonstrate that our open-source, pmx-based approach can accurately resolve the pKa values of coupled residues and observe a substantial performance disparity associated with the lysine partial charges in Amber14SB/Amber99SB*-ILDN, for which an underused fix already exists

Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software

In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host–guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of 1.38kcal/mol in terms of the unsigned error averaged over the whole dataset. Subsequently, we further report on the underlying reasons for discrepancies between our calculations and another submission to the SAMPL7 challenge which employed a similar methodology, but disparate ligand and water force fields. As a result we have uncovered a number of issues in the dihedral parameter definition of the GAFF 2 force field. In addition, we identified particular cases in the molecular topologies where different software packages had a different interpretation of the same force field. This latter observation might be of particular relevance for systematic comparisons of molecular simulation software packages. The aforementioned factors have an influence on the final free energy estimates and need to be considered when performing alchemical calculations

Biomolecular simulations at the exascale: From drug design to organelles and beyond.

The rapid advancement in computational power available for research offers to bring not only quantitative improvements, but also qualitative changes in the field of biomolecular simulation. Here, we review the state of biomolecular dynamics simulations at the threshold to exascale resources becoming available. Both developments in parallel and distributed computing will be discussed, providing a perspective on the state of the art of both. A main focus will be on obtaining binding and conformational free energies, with an outlook to macromolecular complexes and (sub)cellular assemblies