Prediction of protein-ligand binding poses is an essential component for understanding protein-ligand interactions and computer-aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein-ligand docking. Here, we extend the GalaxyDock2 protein-ligand docking algorithm to handle ligand docking to heme proteins. Docking to heme proteins involves increased complexity because the interaction of heme iron and ligand has covalent nature. GalaxyDock2-HEME, a new protein-ligand docking program for heme proteins, has been developed based on GalaxyDock2 by adding an orientation-dependent scoring term to describe heme iron-ligand coordination interaction. This new docking program performs better than other noncommercial docking programs such as EADock with MMBP, AutoDock Vina, PLANTS, LeDock, and GalaxyDock2 on a heme protein-ligand docking benchmark set in which ligands are known to bind iron. In addition, docking results on two other sets of heme protein-ligand complexes in which ligands do not bind iron show that GalaxyDock2-HEME does not have a high bias toward iron binding compared to other docking programs. This implies that the new docking program can distinguish iron binders from noniron binders for heme proteins.

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Because of instability issues presented by metal halide perovskites based on methylammonium (MA), its replacement to Cs has emerged as an alternative to improve the materials' durability. However, the impact of this replacement on electronic properties, especially gap energy and bulk Rashba splitting remains unclear since electrostatic interactions from organic cations can play a crucial role. Through first-principles calculations, we investigated how organic/inorganic cations impact the electronic properties of MAPbI3 and CsPbI3 perovskites. Although at high temperatures the organic cation can assume spherical-like configurations due to its rotation into the cages, our results provide a complete electronic mechanism to show, from a chemical perspective based on ab initio calculations at 0 K , how the MA dipoles suppression can reduce the MAPbI3 gap energy by promoting a degeneracy breaking in the electronic states from the PbI3 framework, while the dipole moment reinforcement is crucial to align theory  ↔  experiment, increasing the bulk Rashba splitting through higher Pb off-centering motifs. The lack of permanent dipole moment in Cs results in CsPbI3 polymorphs with a pronounced Pb on-centering-like feature, which causes suppression in their respective bulk Rashba effect.

Cryo-electron microscopy (cryo-EM) is gaining large attention for high-resolution protein structure determination in solutions. However, a very high percentage of cryo-EM structures correspond to resolutions of 3-5 Å, making the structures difficult to be used in in silico drug design. In this study, we analyze how useful cryo-EM protein structures are for in silico drug design by evaluating ligand docking accuracy. From realistic cross-docking scenarios using medium resolution (3-5 Å) cryo-EM structures and a popular docking tool Autodock-Vina, only 20% of docking succeeded, when the success rate doubles in the same kind of cross-docking but using high-resolution (<2 Å) crystal structures instead. We decipher the reason for failures by decomposing the contribution from resolution-dependent and independent factors. The heterogeneity in the protein side-chain and backbone conformations is identified as the major resolution-dependent factor causing docking difficulty from our analysis, while intrinsic receptor flexibility mainly comprises the resolution-independent factor. We demonstrate the flexibility implementation in current ligand docking tools is able to rescue only a portion of failures (10%), and the limited performance was majorly due to potential structural errors than conformational changes. Our work suggests the strong necessity of more robust method developments on ligand docking and EM modeling techniques in order to fully utilize cryo-EM structures for in silico drug design.

Analysis of the mean squared displacement of species k , (rk2) , as a function of simulation time t constitutes a powerful method for extracting, from a molecular-dynamics (MD) simulation, the tracer diffusion coefficient, Dk* . The statistical error in Dk* is seldom considered, and when it is done, the error is generally underestimated. In this study, we examined the statistics of (rk2)(t) curves generated by solid-state diffusion by means of kinetic Monte Carlo sampling. Our results indicate that the statistical error in Dk* depends, in a strongly interrelated way, on the simulation time, the cell size, and the number of relevant point defects in the simulation cell. Reducing our results to one key quantity-the number of k particles that have jumped at least once-we derive a closed-form expression for the relative uncertainty in Dk* . We confirm the accuracy of our expression through comparisons with self-generated MD diffusion data. With the expression, we formulate a set of simple rules that encourage the efficient use of computational resources for MD simulations.

We present a large set of vertical excitation calculations for the ortho-nitrobenzaldehyde (oNBA) molecule, which exhibits a very challenging excited-state electronic structure like other nitroaromatic compounds. The single-reference methods produce mostly consistent results up to about 5.5 eV. By contrast, the CAS second-order perturbation theory (CASPT2) results depend sensitively on the employed parameters. At the CAS self-consistent field level, the energies of the bright ππ* states are strongly overestimated while doubly excited states appear too low and mix with these ππ* states. This mixing hampers the CASPT2 step, leading to inconsistent results. Only by increasing the number of states in the state-averaging step to about 40-to cover all bright ππ* states embedded in the double excitations-and employing extended multistate CASPT2 could CASPT2 results consistent with experiment be obtained. We assign the four bands in the molecule's spectrum: The weakest band at 3.7 eV arises from the nNO2π* states, the second one at 4.4 eV from the ππ* ( Lb ) state, the shoulder at 5.2 eV from the ππ* ( La ) state, and the maximum at 5.7 eV from the ππ* ( Ba/Bb ) states. We also highlight the importance of modern wave function analysis techniques in elucidating the absorption spectrum of challenging molecules.

Solvent-mediated interactions contribute to ligand binding affinities in computational drug design and provide a challenge for theoretical predictions. In this study, we analyze the solvation free energy of benzene derivatives in water to guide the development of predictive models for solvation free energies and solvent-mediated interactions. We use a spatially resolved analysis of local solvation free energy contributions and define solvation free energy arithmetic, which enable us to construct additive models to describe the solvation of complex compounds. The substituents analyzed in this study are carboxyl and nitro-groups due to their similar sterical requirements but distinct interactions with water. We find that nonadditive solvation free energy contributions are primarily attributed to electrostatics, which are qualitatively reproduced with computationally efficient continuum models. This suggests a promising route for the development of efficient and accurate models for the solvation of complex molecules with varying substitution patterns using solvation arithmetic.

Designing movesets providing high quality protein conformations remains a hard problem, especially when it comes to deform a long protein backbone segment, and a key building block to do so is the so-called tripeptide loop closure (TLC). Consider a tripeptide whose first and last bonds ( N1Cα;1 and Cα;3C3 ) are fixed, and so are all internal coordinates except the six ((ϕ,ψ))i=1,2,3 dihedral angles associated to the three Cα carbons. Under these conditions, the TLC algorithm provides all possible values for these six dihedral angles-there exists at most 16 solutions. TLC moves atoms up to  ∼ 5 Å in one step and retains low energy conformations, whence its pivotal role to design move sets sampling protein loop conformations. In this work, we relax the previous constraints, allowing the last bond ( Cα;3C3 ) to freely move in 3D space-or equivalently in a 5D configuration space. We exhibit necessary geometric constraints in this 5D space for TLC to admit solutions. Our analysis provides key insights on the geometry of solutions for TLC. Most importantly, when using TLC to sample loop conformations based on m consecutive tripeptides along a protein backbone, we obtain an exponential gain in the volume of the 5m -dimensional configuration space to be explored.

The accurate evaluation of electron correlations is highly necessary for the proper descriptions of the electronic structures in strongly correlated molecules, ranging from bond-dissociating molecules, polyradicals, to large conjugated molecules and transition metal complexes. For this purpose, in this paper, a new ab-initio quantum chemistry program Kylin 1.0 for electron correlation calculations at various quantum many-body levels, including configuration interaction (CI), perturbation theory (PT), and density matrix renormalization group (DMRG), is presented. Furthermore, fundamental quantum chemistry methods such as Hartree-Fock self-consistent field (HF-SCF) and the complete active space SCF (CASSCF) are also implemented. The Kylin 1.0 program possesses the following features: (1) a matrix product operator (MPO) formulation-based efficient DMRG implementation for describing static electron correlation within a large active space composed of more than 100 orbitals, supporting both U(1)n × U(1)Sz and U(1)n × SU(2)S symmetries; (2) an efficient second-order DMRG-self-consistent field (SCF) implementation; (3) an externally contracted multi-reference CI (MRCI) and Epstein-Nesbet PT with DMRG reference wave functions for including the remaining dynamic electron correlation outside the large active spaces. In this paper, we introduce the capabilities and numerical benchmark examples of the Kylin 1.0 program.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.