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

Comment on "Deficiencies in molecular dynamics simulation-based prediction of protein-DNA binding free energy landscapes"

Sequence-specific DNA binding transcription factors play an essential role in the transcriptional regulation of all organisms. The development of reliable in silico methods to predict the binding affinity landscapes of transcription factors thus promises to provide rapid screening of transcription factor specificities and, at the same time, yield valuable insight into the atomistic details of the interactions driving those specificities. Recent literature has reported highly discrepant results on the current ability of state-of-the-art atomistic molecular dynamics simulations to reproduce experimental binding free energy landscapes for transcription factors. Here, we resolve one important discrepancy by noting that in the case of alchemical free energy calculations involving base pair mutations, a common convention used in improving end point convergence of mixed potentials in fact can lead to erroneous results. The underlying cause for inaccurate double free energy difference estimates is specific to the particular implementation of the alchemical transformation protocol. Using the Gromacs simulation package, which is not affected by this issue, we obtain free energy landscapes in agreement with the experimental measurements; equivalent results are obtained for a small set of test cases with a modified version of the AMBER package. Our findings provide a consistent and optimistic outlook on the current state of prediction of protein-DNA binding free energy interactions using molecular dynamics simulations and an important precaution for appropriate end point handling in a broad range of free energy calculations

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

Driving forces and structural determinants of steric zipper peptide oligomer formation elucidated by atomistic simulations.

Understanding the structural and energetic requirements of non-fibrillar oligomer formation harbors the potential to decipher an important yet still elusive part of amyloidogenic peptide and protein aggregation. Low-molecular-weight oligomers are described to be transient and polymorphic intermediates in the nucleated self-assembly process to highly ordered amyloid fibers and were additionally found to exhibit a profound cytotoxicity. However, detailed structural information on the oligomeric species involved in the nucleation cannot be readily inferred from experiments. Here, we study the spontaneous assembly of steric zipper peptides from the tau protein, insulin and α-synuclein with atomistic molecular dynamics simulations on the microsecond timescale. Detailed analysis of the forces driving the oligomerization reveals a common two-step process akin to a general condensation-ordering mechanism and thus provides a rational understanding of the molecular basis of peptide self-assembly. Our results suggest that the initial formation of partially ordered peptide oligomers is governed by the solvation free energy, whereas the dynamical ordering and emergence of β-sheets are mainly driven by optimized inter-peptide interactions in the collapsed state. A novel mapping technique based on collective coordinates is employed to highlight similarities and differences in the conformational ensemble of small oligomer structures. Elucidating the dynamical and polymorphic β-sheet oligomer conformations at atomistic detail furthermore suggests complementary sheet packing characteristics similar to steric zipper structures, but with a larger heterogeneity in the strand alignment pattern and sheet-to-sheet arrangements compared to the cross-β motif found in the fibrillar or crystalline states

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

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

Chemical Space Exploration with Active Learning and Alchemical Free Energies

Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate toward potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds, high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives