Showing papers by "Adrian E. Roitberg published in 2022"
TL;DR: This work developed the Python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input and provides an extension of the ANI model, dubbed ANI-2xt, which can accurately predict the energies and is several orders of magnitude faster than DFT methods.
Abstract: Computational programs accelerate the chemical discovery processes but often need proper three-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing a huge space of stereoisomers and conformers. We developed the Python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input. Auto3D is based on state-of-the-art algorithms and can automatize the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization, and ranking process. Tested on 50 molecules with multiple unspecified stereocenters, Auto3D is guaranteed to find the stereoconfiguration that yields the lowest-energy conformer. With Auto3D, we provide an extension of the ANI model. The new model, dubbed ANI-2xt, is trained on a tautomer-rich data set. ANI-2xt is benchmarked with DFT methods on geometry optimization and electronic and Gibbs free energy calculations. Compared with ANI-2x, ANI-2xt provides a 42% error reduction for tautomeric reaction energy calculations when using the gold-standard coupled-cluster calculation as the reference. ANI-2xt can accurately predict the energies and is several orders of magnitude faster than DFT methods.
7 citations
TL;DR: In this article , a diastereoselective indole-dearomative Cope rearrangement was proposed, where the key substrates are rapidly assembled from simple starting materials, resulting in many successful examples.
3 citations
TL;DR: In this paper , pH replica exchange molecular dynamics (pH-REMD) simulations of GS in apo and substrate-bound forms were performed to sample the protonation states of the catalytic dyad.
Abstract: γ-Secretase (GS) is an intramembrane aspartyl protease that participates in the sequential cleavage of C99 to generate different isoforms of the amyloid-β (Aβ) peptides that are associated with the development of Alzheimer's disease. Due to its importance in the proteolytic processing of C99 by GS, we performed pH replica exchange molecular dynamics (pH-REMD) simulations of GS in its apo and substrate-bound forms to sample the protonation states of the catalytic dyad. We found that the catalytic dyad is deprotonated at physiological pH in our apo form, but the presence of the substrate at the active site displaces its monoprotonated state toward physiological pH. Our results show that Asp257 acts as the general base and Asp385 as the general acid during the cleavage mechanism. We identified different amino acids such as Lys265, Arg269, and the PAL motif interacting with the catalytic dyad and promoting changes in its acid-base behavior. Finally, we also found a significant pKa shift of Glu280 related to the internalization of TM6-CT in the GS-apo form. Our study provides critical mechanistic insight into the GS mechanism and the basis for future research on the genesis of Aβ peptides and the development of Alzheimer's disease.
2 citations
TL;DR: A computational protocol to constrain rotatable torsional angles and other geometric parameters for a molecule whose geometry is described by Cartesian coordinates is developed and it is expected that the computational algorithms and protocols presented in this work will have great applications in improving the quality of an existing small-molecule MMFF.
Abstract: An efficient yet accurate method for producing a large amount of energy data for molecular mechanical force field (MMFF) parameterization is on demand, especially for torsional angle parameters which are typically derived to reproduce ab initio rotational profiles or torsional potential energy surfaces (PESs). Recently, an active learning potential (ANI-1x) for organic molecules which can produce smooth and physically meaningful PESs has been developed. The high efficiency and accuracy make ANI-1x especially attractive for geometry optimization at low cost. To apply the ANI-1x potential in MMFF parameterization, one needs to perform constrained geometry optimization. In this work, we first developed a computational protocol to constrain rotatable torsional angles and other geometric parameters for a molecule whose geometry is described by Cartesian coordinates. The constraint is successfully achieved by force projection for the two conjugated gradient (CG) algorithms. We then conducted large-scale assessments on ANI-1x along with four different optimization algorithms in reproducing DFT energies and geometries for two CG algorithms, CG backtracking line search (CG-BS) and CG Wolfe line search (CG-WS), and two quasi-Newton algorithms, Broyden-Fletcher-Goldfarb-Shanno (BFGS) and low-memory BFGS (L-BFGS). Note that CG-BS is a new algorithm we developed in this work. All four algorithms take the ANI energies and forces to optimize a molecule geometry. Last, we conducted a large-scale assessment of applying ANI-1x in MMFF development in three aspects. First, we performed full optimizations for 100 drug molecules, each consisting of five distinct conformations. The average root-mean-square error (RMSE) between ANI-1x and DFT is about 1.3 kcal/mol, and the root-mean-square displacement (RMSD) of heavy atoms is about 0.35 Å. Second, we generated torsional PESs for 160 organic molecules, and constrained optimizations were performed for up to 18 conformations for each PES. We found that the RMSE of all the conformers is 1.23 kcal/mol. Last, we carried out constrained optimizations for alanine dipeptide with both ϕ and φ angles being frozen. The Ramachandran plots indicate that the two CG algorithms in conjunction with the ANI-1x potential could well reproduce the DFT-optimized geometries and torsional PESs. We concluded that CG-BS and CG-WS are good choices for generating PESs, while CG-WS or BFGS is ideal for performing full geometry optimization. With the continuously increased quality of ANI, it is expected that the computational algorithms and protocols presented in this work will have great applications in improving the quality of an existing small-molecule MMFF.
2 citations
TL;DR: In this article , the NEXMD-SANDER exchange has been optimized in order to achieve excited-state adiabatic dynamics simulations of large conjugated materials in a QM/MM environment, such as an explicit solvent.
Abstract: We present a method to link the Nonadiabatic EXcited-state Molecular Dynamics (NEXMD) package to the SANDER package supplied by AMBERTOOLS to provide excited-state adiabatic quantum mechanics/molecular mechanics (QM/MM) simulations. NEXMD is a computational package particularly developed to perform simulations of the photoexcitation and subsequent nonadiabatic electronic and vibrational energy relaxation in large multichromophoric conjugated molecules involving several coupled electronic excited states. The NEXMD-SANDER exchange has been optimized in order to achieve excited-state adiabatic dynamics simulations of large conjugated materials in a QM/MM environment, such as an explicit solvent. Dynamics of a substituted polyphenylene vinylene oligomer (PPV3-NO2) in vacuum and different explicit solvents has been used as a test case by performing comparative analysis of changes in its optical spectrum, state-dependent conformational changes, and quantum bond orderings. The method has been tested and compared with respect to previous implicit solvent implementations. Also, the impact on the expansion of the QM region by including a variable number of solvent molecules has been analyzed. Altogether, these results encourage future implementations of NEXMD simulations using the same combination of methods.
1 citations
TL;DR: Reported are structure‐property‐function relationships associated with a class of cyclic thiosulfonate molecules—disulfide‐bond disrupting agents (DDAs)—with the ability to downregulate the Epidermal Growth Factor Receptor (HER) family in parallel and selectively induce apoptosis of EGFR+ or HER2+ breast cancer cells.
Abstract: Reported are structure‐property‐function relationships associated with a class of cyclic thiosulfonate molecules—disulfide‐bond disrupting agents (DDAs)—with the ability to downregulate the Epidermal Growth Factor Receptor (HER) family in parallel and selectively induce apoptosis of EGFR+ or HER2+ breast cancer cells. Recent findings have revealed that the DDA mechanism of action involves covalent binding to the thiol(ate) from the active site cysteine residue of members of the protein disulfide isomerase (PDI) family. Reported is how structural modifications to the pharmacophore can alter the anticancer activity of cyclic thiosulfonates by tuning the dynamics of thiol‐thiosulfonate exchange reactions, and the studies reveal a correlation between the biological potency and thiol‐reactivity. Specificity of the cyclic thiosulfonate ring‐opening reaction by a nucleophilic attack can be modulated by substituent addition to a parent scaffold. Lead compound optimization efforts are also reported, and have resulted in a considerable decrease of the IC50/IC90 values toward HER‐family overexpressing breast cancer cells.
TL;DR: First in class multifunctional non classical antifolates inhibits thymidylate synthase and extends survival in pancreatic cancer model and is well tolerated with equal efficacy using either intraperitoneal or oral administration.
Abstract:
Thymidylate synthase (TS) inhibitors are an integral component of chemotherapy regimens for difficult to treat cancer subtypes. Despite initial therapeutic benefit, current inhibitors induce TS overexpression or alter folate transport metabolism feedback pathways that tumor cells exploit for drug resistance. Here we report a small molecule TS inhibitor that exhibits i) enhanced antitumor activity as compared to current fluoropyrimidines and antifolates without inducing TS overexpression, ii) is structurally distinct from classical antifolates, iii) extends survival in a pancreatic tumor mouse model, iv) and is well tolerated with equal efficacy using either intraperitoneal or oral administration. Mechanistically, we confirm the compound is a multifunctional non classical antifolate and through a series of analogues identify structural features allowing direct TS inhibition while also maintaining the ability to inhibit dihydrofolate reductase (DHFR). Collectively, this work identifies new non classical antifolate inhibitors that optimize inhibition of thymidylate biosynthesis with a favorable safety profile highlighting potential for enhanced cancer therapy.
Citation Format: Maria V. Guijarro, Patrick C. Kellish, Peter E. Dib, Nicholas G. Paciaroni, Akbar Nawab, Jacob Andring, Lidia Kulemina, Nicholas V. Borrero, Carlos Modenutti, Richard L. Bennett, Daniil Shabashvili, Jonathan D. Licht, Robert McKenna, Adrian Roitberg, Robert W. Huigens, Frederic J. Kaye, Maria Zajac-Kaye. First in class multifunctional non classical antifolates inhibits thymidylate synthase and extends survival in pancreatic cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4055.