Publications by authors named "Richard A Friesner"

151 Publications

Highly efficient implementation of the analytical gradients of pseudospectral time-dependent density functional theory.

J Chem Phys 2021 Jul;155(2):024115

Department of Chemistry, Columbia University, New York, New York 10027, USA.

The accuracy and efficiency of time-dependent density functional theory (TDDFT) excited state gradient calculations using the pseudospectral method are presented. TDDFT excited state geometry optimizations of the G2 test set molecules, the organic fluorophores with large Stokes shifts, and the Pt-complexes show that the pseudospectral method gives average errors of 0.01-0.1 kcal/mol for the TDDFT excited state energy, 0.02-0.06 pm for the bond length, and 0.02-0.12° for the bond angle when compared to the results from conventional TDDFT. TDDFT gradient calculations of fullerenes (C, n up to 540) with the B3LYP functional and 6-31G** basis set show that the pseudospectral method provides 8- to 14-fold speedups in the total wall clock time over the conventional methods. The pseudospectral TDDFT gradient calculations with a diffuse basis set give higher speedups than the calculations for the same basis set without diffuse functions included.
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http://dx.doi.org/10.1063/5.0055379DOI Listing
July 2021

prediction of annihilators for triplet-triplet annihilation upconversion auxiliary-field quantum Monte Carlo.

Chem Sci 2020 Nov 17;12(3):1068-1079. Epub 2020 Nov 17.

Department of Chemistry, Columbia University 3000 Broadway New York NY 10027 USA

The energy of the lowest-lying triplet state (T1) relative to the ground and first-excited singlet states (S0, S1) plays a critical role in optical multiexcitonic processes of organic chromophores. Focusing on triplet-triplet annihilation (TTA) upconversion, the S0 to T1 energy gap, known as the triplet energy, is difficult to measure experimentally for most molecules of interest. predictions can provide a useful alternative, however low-scaling electronic structure methods such as the Kohn-Sham and time-dependent variants of Density Functional Theory (DFT) rely heavily on the fraction of exact exchange chosen for a given functional, and tend to be unreliable when strong electronic correlation is present. Here, we use auxiliary-field quantum Monte Carlo (AFQMC), a scalable electronic structure method capable of accurately describing even strongly correlated molecules, to predict the triplet energies for a series of candidate annihilators for TTA upconversion, including 9,10 substituted anthracenes and substituted benzothiadiazole (BTD) and benzoselenodiazole (BSeD) compounds. We compare our results to predictions from a number of commonly used DFT functionals, as well as DLPNO-CCSD(T), a localized approximation to coupled cluster with singles, doubles, and perturbative triples. Together with S1 estimates from absorption/emission spectra, which are well-reproduced by TD-DFT calculations employing the range-corrected hybrid functional CAM-B3LYP, we provide predictions regarding the thermodynamic feasibility of upconversion by requiring (a) the measured T1 of the sensitizer exceeds that of the calculated T1 of the candidate annihilator, and (b) twice the T1 of the annihilator exceeds its S1 energetic value. We demonstrate a successful example of discovery of a novel annihilator, phenyl-substituted BTD, and present experimental validation low temperature phosphorescence and the presence of upconverted blue light emission when coupled to a platinum octaethylporphyrin (PtOEP) sensitizer. The BTD framework thus represents a new class of annihilators for TTA upconversion. Its chemical functionalization, guided by the computational tools utilized herein, provides a promising route towards high energy (violet to near-UV) emission.
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http://dx.doi.org/10.1039/d0sc03381bDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179011PMC
November 2020

OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space.

J Chem Theory Comput 2021 Jul 7;17(7):4291-4300. Epub 2021 Jun 7.

Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States.

We report on the development and validation of the OPLS4 force field. OPLS4 builds upon our previous work with OPLS3e to improve model accuracy on challenging regimes of drug-like chemical space that includes molecular ions and sulfur-containing moieties. A novel parametrization strategy for charged species, which can be extended to other systems, is introduced. OPLS4 leads to improved accuracy on benchmarks that assess small-molecule solvation and protein-ligand binding.
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http://dx.doi.org/10.1021/acs.jctc.1c00302DOI Listing
July 2021

Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding.

J Chem Theory Comput 2021 Apr 29;17(4):2630-2639. Epub 2021 Mar 29.

Department of Chemistry, Columbia University, 3000 Broadway, MC 3110, New York, New York 10036, United States.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.
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http://dx.doi.org/10.1021/acs.jctc.1c00136DOI Listing
April 2021

Cryo-EM Structures of SARS-CoV-2 Spike without and with ACE2 Reveal a pH-Dependent Switch to Mediate Endosomal Positioning of Receptor-Binding Domains.

Cell Host Microbe 2020 12 17;28(6):867-879.e5. Epub 2020 Nov 17.

Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. Electronic address:

The SARS-CoV-2 spike employs mobile receptor-binding domains (RBDs) to engage the human ACE2 receptor and to facilitate virus entry, which can occur through low-pH-endosomal pathways. To understand how ACE2 binding and low pH affect spike conformation, we determined cryo-electron microscopy structures-at serological and endosomal pH-delineating spike recognition of up to three ACE2 molecules. RBDs freely adopted "up" conformations required for ACE2 interaction, primarily through RBD movement combined with smaller alterations in neighboring domains. In the absence of ACE2, single-RBD-up conformations dominated at pH 5.5, resolving into a solitary all-down conformation at lower pH. Notably, a pH-dependent refolding region (residues 824-858) at the spike-interdomain interface displayed dramatic structural rearrangements and mediated RBD positioning through coordinated movements of the entire trimer apex. These structures provide a foundation for understanding prefusion-spike mechanics governing endosomal entry; we suggest that the low pH all-down conformation potentially facilitates immune evasion from RBD-up binding antibody.
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http://dx.doi.org/10.1016/j.chom.2020.11.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7670890PMC
December 2020

A pH-dependent switch mediates conformational masking of SARS-CoV-2 spike.

bioRxiv 2020 Jul 4. Epub 2020 Jul 4.

SARS-CoV-2 has emerged as a global pathogen, sparking urgent vaccine development efforts with the trimeric spike. However, the inability of antibodies like CR3022, which binds a cryptic spike epitope with nanomolar affinity, to neutralize virus, suggests a spike-based means of neutralization escape. Here, we show the SARS-CoV-2 spike to have 10% the unfolding enthalpy of a globular protein at physiological pH, where it is recognized by antibodies like CR3022, and up to 10-times more unfolding enthalpy at endosomal pH, where it sheds such antibodies, suggesting that the spike evades potentially neutralizing antibody through a pH-dependent mechanism of conformational masking. To understand the compatibility of this mechanism with ACE2-receptor interactions, we carried out binding measurements and determined cryo-EM structures of the spike recognizing up to three ACE2 molecules at both physiological and endosomal pH. In the absence of ACE2, cryo-EM analyses indicated lower pH to reduce conformational heterogeneity. Single-receptor binding domain (RBD)-up conformations dominated at pH 5.5, resolving into a locked all-down conformation at lower pH through lowering of RBD and refolding of a pH-dependent switch. Notably, the emerging Asp614Gly strain partially destabilizes the switch that locks RBD down, thereby enhancing functional interactions with ACE2 while reducing evasion by conformational masking.
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http://dx.doi.org/10.1101/2020.07.04.187989DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337388PMC
July 2020

Multiple Stable Isoprene-Ozone Complexes Reveal Complex Entrance Channel Dynamics in the Isoprene + Ozone Reaction.

J Am Chem Soc 2020 06 5;142(24):10806-10813. Epub 2020 Jun 5.

Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

Accurately characterizing isoprene ozonolysis continues to challenge atmospheric chemists. The reaction is believed to be a spontaneous, concerted cycloaddition. However, little information is available about the entrance channel and isoprene-ozone complexes thought to define the long-range portion of the reaction coordinate. Our coupled cluster and auxiliary field quantum Monte Carlo calculations predict multiple stable isoprene-ozone van der Waals complexes for -isoprene in the gas phase with moderate association energies. These results indicate that long-range dynamics in the isoprene-ozone entrance channel can impact the overall reaction in the troposphere and provide the spectroscopic information necessary to extend the microwave characterization of isoprene ozonolysis to prereactive complexes. At the air-water interface, Born-Oppenheimer molecular dynamics simulations indicate that the cycloaddition reaction between ozone and -isoprene follows a stepwise mechanism, which is quite distinct from our proposed gas-phase mechanism and occurs on a femtosecond time scale. The stepwise nature of isoprene ozonolysis on the aqueous surface is more consistent with the DeMore mechanism than with the Criegee mechanism suggested by the gas-phase calculations, suggesting that the reaction media may play an important role in the reaction. Overall, these predictions aim to provide a missing fundamental piece of molecular insight into isoprene ozonolysis, which has broad tropospheric implications due to its critical role as a nighttime source of hydroxyl radicals.
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http://dx.doi.org/10.1021/jacs.0c02360DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609128PMC
June 2020

Predicting Ligand-Dissociation Energies of 3d Coordination Complexes with Auxiliary-Field Quantum Monte Carlo.

J Chem Theory Comput 2020 May 29;16(5):3041-3054. Epub 2020 Apr 29.

Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States.

Transition-metal complexes are ubiquitous in biology and chemical catalysis, yet they remain difficult to accurately describe with methods because of the presence of a large degree of dynamic electron correlation, and, in some cases, strong static correlation which results from a manifold of low-lying states. Progress has been hindered by a scarcity of high-quality gas-phase experimental data, while exact predictions are usually computationally unaffordable because of the large size of the relevant complexes. In this work, we present a data set of 34 tetrahedral, square planar, and octahedral 3d metal-containing complexes with gas-phase ligand-dissociation energies that have reported uncertainties of ≤2 kcal/mol. We perform all-electron phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) calculations utilizing multideterminant trial wave functions selected by a black box procedure. We compare the results with those from the density functional theory (DFT) with the B3LYP, B97, M06, PBE0, ωB97X-V, and DSD-PBEP86/2013 functionals and a localized orbital variant of the coupled cluster theory with single, double, and perturbative triple excitations (DLPNO-CCSD(T)). We find mean averaged errors of 1.07 ± 0.27 kcal/mol for our most sophisticated ph-AFQMC approach versus 2.81 kcal/mol for DLPNO-CCSD(T) and 1.49-3.78 kcal/mol for DFT. We find maximum errors of 2.96 ± 1.71 kcal/mol for our best ph-AFQMC method versus 9.15 kcal/mol for DLPNO-CCSD(T) and 5.98-13.69 kcal/mol for DFT. The reasonable performance of a number of DFT functionals is in stark contrast to the much poorer accuracy previously demonstrated for diatomic species, suggesting a moderation in electron correlation because of ligand coordination in most cases. However, the unpredictably large errors for a small subset of cases with both DFT and DLPNO-CCSD(T) methods leave cause for concern, especially in light of the unreliability of common multireference indicators. In contrast, the robust and, in principle, systematically improvable results of ph-AFQMC for these realistic complexes establish the method as a useful tool for elucidating the electronic structure of transition-metal-containing complexes and predicting their gas-phase properties.
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http://dx.doi.org/10.1021/acs.jctc.0c00070DOI Listing
May 2020

Accurate Quantum Chemical Calculation of Ionization Potentials: Validation of the DFT-LOC Approach via a Large Data Set Obtained from Experiments and Benchmark Quantum Chemical Calculations.

J Chem Theory Comput 2020 Apr 20;16(4):2109-2123. Epub 2020 Mar 20.

Department of Chemistry, Columbia University, New York, New York 10027, United States.

Density functional theory (DFT) is known to often fail when calculating thermodynamic values, such as ionization potentials (IPs), due to nondynamical error (i.e., the self-interaction term). Localized orbital corrections (LOCs), derived from assigning corresponding corrections for the atomic orbitals, bonds, and paired and unpaired electrons, are utilized to correct the IPs calculated from DFT. Some of the assigned parameters, which are physically due to the contraction of and change of the environment around a bond, depend on identifying the location in the molecule from which the electron is removed using differences in the charge density between neutral and oxidized species. In our training set, various small organic and inorganic molecules from the literature with the reported experimental IP were collected using the NIST database. For certain molecules with uncertain or no experimental measurements, we obtain the IP using coupled cluster theory and auxiliary field quantum Monte Carlo. After applying these corrections, as generated by least-squares regression, LOC reduces the mean absolute deviation (MAD) of the training set from 0.143 to 0.046 eV ( = 0.895), and LOC reduces the MAD of the test set from 0.192 to 0.097 eV ( = 0.833).
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http://dx.doi.org/10.1021/acs.jctc.9b00875DOI Listing
April 2020

Singlet-Triplet Energy Gaps of Organic Biradicals and Polyacenes with Auxiliary-Field Quantum Monte Carlo.

J Chem Theory Comput 2019 Sep 19;15(9):4924-4932. Epub 2019 Aug 19.

Department of Chemistry , Columbia University , 3000 Broadway , New York , New York 10027 , United States.

The energy gap between the lowest-lying singlet and triplet states is an important quantity in chemical photocatalysis, with relevant applications ranging from triplet fusion in optical upconversion to the design of organic light-emitting devices. The prediction of singlet-triplet (ST) gaps is challenging due to the potentially biradical nature of the involved states, combined with the potentially large size of relevant molecules. In this work, we show that phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) can accurately predict ST gaps for chemical systems with singlet states of highly biradical nature, including a set of 13 small molecules and the -, -, and - isomers of benzyne. With respect to gas-phase experiments, ph-AFQMC using CASSCF trial wave functions achieves a mean averaged error of ∼1 kcal/mol. Furthermore, we find that in the context of a spin-projection technique, ph-AFQMC using unrestricted single-determinant trial wave functions, which can be readily obtained for even very large systems, produces equivalently high accuracy. We proceed to show that this scalable methodology is capable of yielding accurate ST gaps for all linear polyacenes for which experimental measurements exist, that is, naphthalene, anthracene, tetracene, and pentacene. Our results suggest a protocol for selecting either unrestricted Hartree-Fock or Kohn-Sham orbitals for the single-determinant trial wave function, based on the extent of spin-contamination. These findings pave the way for future investigations of specific photochemical processes involving large molecules with substantial biradical character.
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http://dx.doi.org/10.1021/acs.jctc.9b00534DOI Listing
September 2019

On Achieving High Accuracy in Quantum Chemical Calculations of 3 d Transition Metal-Containing Systems: A Comparison of Auxiliary-Field Quantum Monte Carlo with Coupled Cluster, Density Functional Theory, and Experiment for Diatomic Molecules.

J Chem Theory Comput 2019 Apr 27;15(4):2346-2358. Epub 2019 Mar 27.

Department of Chemistry , Columbia University , 3000 Broadway , New York , New York 10027 , United States.

The bond dissociation energies of a set of 44 3 d transition metal-containing diatomics are computed with phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) utilizing a correlated sampling technique. We investigate molecules with H, N, O, F, Cl, and S ligands, including those in the 3dMLBE20 database first compiled by Truhlar and co-workers with calculated and experimental values that have since been revised by various groups. In order to make a direct comparison of the accuracy of our ph-AFQMC calculations with previously published results from 10 DFT functionals, CCSD(T), and icMR-CCSD(T), we establish an objective selection protocol which utilizes the most recent experimental results except for a few cases with well-specified discrepancies. With the remaining set of 41 molecules, we find that ph-AFQMC gives robust agreement with experiment superior to that of all other methods, with a mean absolute error (MAE) of 1.4(4) kcal/mol and maximum error of 3(3) kcal/mol (parentheses account for reported experimental uncertainties and the statistical errors of our ph-AFQMC calculations). In comparison, CCSD(T) and B97, the best performing DFT functional considered here, have MAEs of 2.8 and 3.7 kcal/mol, respectively, and maximum errors in excess of 17 kcal/mol (for the CoS diatomic). While a larger and more diverse data set would be required to demonstrate that ph-AFQMC is truly a benchmark method for transition metal systems, our results indicate that the method has tremendous potential, exhibiting unprecedented consistency and accuracy compared to other approximate quantum chemical approaches.
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http://dx.doi.org/10.1021/acs.jctc.9b00083DOI Listing
April 2019

Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein-Protein Interfaces.

J Mol Biol 2019 03 16;431(7):1481-1493. Epub 2019 Feb 16.

Department of Chemistry, Columbia University, 3000 Broadway, MC 3178, New York, NY 10027, USA.

Building on the substantial progress that has been made in using free energy perturbation (FEP) methods to predict the relative binding affinities of small molecule ligands to proteins, we have previously shown that results of similar quality can be obtained in predicting the effect of mutations on the binding affinity of protein-protein complexes. However, these results were restricted to mutations which did not change the net charge of the side chains due to known difficulties with modeling perturbations involving a change in charge in FEP. Various methods have been proposed to address this problem. Here we apply the co-alchemical water approach to study the efficacy of FEP calculations of charge changing mutations at the protein-protein interface for the antibody-gp120 system investigated previously and three additional complexes. We achieve an overall root mean square error of 1.2 kcal/mol on a set of 106 cases involving a change in net charge selected by a simple suitability filter using side-chain predictions and solvent accessible surface area to be relevant to a biologic optimization project. Reasonable, although less precise, results are also obtained for the 44 more challenging mutations that involve buried residues, which may in some cases require substantial reorganization of the local protein structure, which can extend beyond the scope of a typical FEP simulation. We believe that the proposed prediction protocol will be of sufficient efficiency and accuracy to guide protein engineering projects for which optimization and/or maintenance of a high degree of binding affinity is a key objective.
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http://dx.doi.org/10.1016/j.jmb.2019.02.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6453258PMC
March 2019

OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules.

J Chem Theory Comput 2019 Mar 4;15(3):1863-1874. Epub 2019 Mar 4.

Schrodinger, Inc. , 120 West 45th Street , New York , New York 10036 , United States.

Building upon the OPLS3 force field we report on an enhanced model, OPLS3e, that further extends its coverage of medicinally relevant chemical space by addressing limitations in chemotype transferability. OPLS3e accomplishes this by incorporating new parameter types that recognize moieties with greater chemical specificity and integrating an on-the-fly parametrization approach to the assignment of partial charges. As a consequence, OPLS3e leads to greater accuracy against performance benchmarks that assess small molecule conformational propensities, solvation, and protein-ligand binding.
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http://dx.doi.org/10.1021/acs.jctc.8b01026DOI Listing
March 2019

Modeling the value of predictive affinity scoring in preclinical drug discovery.

Curr Opin Struct Biol 2018 10 12;52:103-110. Epub 2018 Oct 12.

Schrodinger, Inc., 120 West 45th Street, New York, NY 10036, United States.

Drug discovery is widely recognized to be a difficult and costly activity in large part due to the challenge of identifying chemical matter which simultaneously optimizes multiple properties, one of which is affinity for the primary biological target. Further, many of these properties are difficult to predict ahead of expensive and time-consuming compound synthesis and experimental testing. Here we highlight recent work to develop compound affinity prediction models, and extensively investigate the value such models may provide to preclinical drug discovery. We demonstrate that the ability of these models to improve the overall probability of success is crucially dependent on the shape of the error distribution, not just the root-mean-square error. In particular, while scoring more molecule ideas generally improves the probability of project success when the error distribution is Gaussian, fat-tail distributions such as a Cauchy distribution, can lead to a situation where scoring more ideas actually decreases the overall probability of success.
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http://dx.doi.org/10.1016/j.sbi.2018.09.002DOI Listing
October 2018

Phaseless Auxiliary-Field Quantum Monte Carlo on Graphical Processing Units.

J Chem Theory Comput 2018 Aug 2;14(8):4109-4121. Epub 2018 Jul 2.

Department of Chemistry , Columbia University , 3000 Broadway , New York , New York 10027 , United States.

We present an implementation of phaseless Auxiliary-Field Quantum Monte Carlo (ph-AFQMC) utilizing graphical processing units (GPUs). The AFQMC method is recast in terms of matrix operations which are spread across thousands of processing cores and are executed in batches using custom Compute Unified Device Architecture kernels and the GPU-optimized cuBLAS matrix library. Algorithmic advances include a batched Sherman-Morrison-Woodbury algorithm to quickly update matrix determinants and inverses, density-fitting of the two-electron integrals, an energy algorithm involving a high-dimensional precomputed tensor, and the use of single-precision floating point arithmetic. These strategies accelerate ph-AFQMC calculations with both single- and multideterminant trial wave functions, though particularly dramatic wall-time reductions are achieved for the latter. For typical calculations we find speed-ups of roughly 2 orders of magnitude using just a single GPU card compared to a single modern CPU core. Furthermore, we achieve near-unity parallel efficiency using 8 GPU cards on a single node and can reach moderate system sizes via a local memory-slicing approach. We illustrate the robustness of our implementation on hydrogen chains of increasing length and through the calculation of all-electron ionization potentials of the first-row transition metal atoms. We compare long imaginary-time calculations utilizing a population control algorithm with our previously published correlated sampling approach and show that the latter improves not only the efficiency but also the accuracy of the computed ionization potentials. Taken together, the GPU implementation combined with correlated sampling provides a compelling computational method that will broaden the application of ph-AFQMC to the description of realistic correlated electronic systems.
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http://dx.doi.org/10.1021/acs.jctc.8b00342DOI Listing
August 2018

Automated Transition State Search and Its Application to Diverse Types of Organic Reactions.

J Chem Theory Comput 2017 Nov 17;13(11):5780-5797. Epub 2017 Oct 17.

Department of Chemistry, Columbia University , 3000 Broadway, New York, New York 10027, United States.

Transition state search is at the center of multiple types of computational chemical predictions related to mechanistic investigations, reactivity and regioselectivity predictions, and catalyst design. The process of finding transition states in practice is, however, a laborious multistep operation that requires significant user involvement. Here, we report a highly automated workflow designed to locate transition states for a given elementary reaction with minimal setup overhead. The only essential inputs required from the user are the structures of the separated reactants and products. The seamless workflow combining computational technologies from the fields of cheminformatics, molecular mechanics, and quantum chemistry automatically finds the most probable correspondence between the atoms in the reactants and the products, generates a transition state guess, launches a transition state search through a combined approach involving the relaxing string method and the quadratic synchronous transit, and finally validates the transition state via the analysis of the reactive chemical bonds and imaginary vibrational frequencies as well as by the intrinsic reaction coordinate method. Our approach does not target any specific reaction type, nor does it depend on training data; instead, it is meant to be of general applicability for a wide variety of reaction types. The workflow is highly flexible, permitting modifications such as a choice of accuracy, level of theory, basis set, or solvation treatment. Successfully located transition states can be used for setting up transition state guesses in related reactions, saving computational time and increasing the probability of success. The utility and performance of the method are demonstrated in applications to transition state searches in reactions typical for organic chemistry, medicinal chemistry, and homogeneous catalysis research. In particular, applications of our code to Michael additions, hydrogen abstractions, Diels-Alder cycloadditions, carbene insertions, and an enzyme reaction model involving a molybdenum complex are shown and discussed.
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http://dx.doi.org/10.1021/acs.jctc.7b00764DOI Listing
November 2017

Advancing Drug Discovery through Enhanced Free Energy Calculations.

Acc Chem Res 2017 07 5;50(7):1625-1632. Epub 2017 Jul 5.

Department of Chemistry, Columbia University , 3000 Broadway, New York, New York 10027, United States.

A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.
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http://dx.doi.org/10.1021/acs.accounts.7b00083DOI Listing
July 2017

Chemical Transformations Approaching Chemical Accuracy via Correlated Sampling in Auxiliary-Field Quantum Monte Carlo.

J Chem Theory Comput 2017 Jun 16;13(6):2667-2680. Epub 2017 May 16.

Department of Chemistry, Columbia University , 3000 Broadway, New York, New York 10027, United States.

The exact and phaseless variants of auxiliary-field quantum Monte Carlo (AFQMC) have been shown to be capable of producing accurate ground-state energies for a wide variety of systems including those which exhibit substantial electron correlation effects. The computational cost of performing these calculations has to date been relatively high, impeding many important applications of these approaches. Here we present a correlated sampling methodology for AFQMC which relies on error cancellation to dramatically accelerate the calculation of energy differences of relevance to chemical transformations. In particular, we show that our correlated sampling-based AFQMC approach is capable of calculating redox properties, deprotonation free energies, and hydrogen abstraction energies in an efficient manner without sacrificing accuracy. We validate the computational protocol by calculating the ionization potentials and electron affinities of the atoms contained in the G2 test set and then proceed to utilize a composite method, which treats fixed-geometry processes with correlated sampling-based AFQMC and relaxation energies via MP2, to compute the ionization potential, deprotonation free energy, and the O-H bond disocciation energy of methanol, all to within chemical accuracy. We show that the efficiency of correlated sampling relative to uncorrelated calculations increases with system and basis set size and that correlated sampling greatly reduces the required number of random walkers to achieve a target statistical error. This translates to CPU-time speed-up factors of 55, 25, and 24 for the ionization potential of the K atom, the deprotonation of methanol, and hydrogen abstraction from the O-H bond of methanol, respectively. We conclude with a discussion of further efficiency improvements that may open the door to the accurate description of chemical processes in complex systems.
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http://dx.doi.org/10.1021/acs.jctc.7b00224DOI Listing
June 2017

A Critical Review of Validation, Blind Testing, and Real- World Use of Alchemical Protein-Ligand Binding Free Energy Calculations.

Curr Top Med Chem 2017 ;17(23):2577-2585

Department of Chemistry, Columbia University, 3000 Broadway, New York, NY 10027, United States.

Protein-ligand binding is among the most fundamental phenomena underlying all molecular biology, and a greater ability to more accurately and robustly predict the binding free energy of a small molecule ligand for its cognate protein is expected to have vast consequences for improving the efficiency of pharmaceutical drug discovery. We briefly reviewed a number of scientific and technical advances that have enabled alchemical free energy calculations to recently emerge as a preferred approach, and critically considered proper validation and effective use of these techniques. In particular, we characterized a selection bias effect which may be important in prospective free energy calculations, and introduced a strategy to improve the accuracy of the free energy predictions.
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http://dx.doi.org/10.2174/1568026617666170414142131DOI Listing
September 2017

Accurate Modeling of Scaffold Hopping Transformations in Drug Discovery.

J Chem Theory Comput 2017 01 9;13(1):42-54. Epub 2016 Dec 9.

Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States.

The accurate prediction of protein-ligand binding free energies remains a significant challenge of central importance in computational biophysics and structure-based drug design. Multiple recent advances including the development of greatly improved protein and ligand molecular mechanics force fields, more efficient enhanced sampling methods, and low-cost powerful GPU computing clusters have enabled accurate and reliable predictions of relative protein-ligand binding free energies through the free energy perturbation (FEP) methods. However, the existing FEP methods can only be used to calculate the relative binding free energies for R-group modifications or single-atom modifications and cannot be used to efficiently evaluate scaffold hopping modifications to a lead molecule. Scaffold hopping or core hopping, a very common design strategy in drug discovery projects, is critical not only in the early stages of a discovery campaign where novel active matter must be identified but also in lead optimization where the resolution of a variety of ADME/Tox problems may require identification of a novel core structure. In this paper, we introduce a method that enables theoretically rigorous, yet computationally tractable, relative protein-ligand binding free energy calculations to be pursued for scaffold hopping modifications. We apply the method to six pharmaceutically interesting cases where diverse types of scaffold hopping modifications were required to identify the drug molecules ultimately sent into the clinic. For these six diverse cases, the predicted binding affinities were in close agreement with experiment, demonstrating the wide applicability and the significant impact Core Hopping FEP may provide in drug discovery projects.
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http://dx.doi.org/10.1021/acs.jctc.6b00991DOI Listing
January 2017

Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1.

J Mol Biol 2017 04 28;429(7):930-947. Epub 2016 Nov 28.

Department of Chemistry, Columbia University, 3000 Broadway, MC 3178, New York, NY 10027, USA. Electronic address:

Direct calculation of relative binding affinities between antibodies and antigens is a long-sought goal. However, despite substantial efforts, no generally applicable computational method has been described. Here, we describe a systematic free energy perturbation (FEP) protocol and calculate the binding affinities between the gp120 envelope glycoprotein of HIV-1 and three broadly neutralizing antibodies (bNAbs) of the VRC01 class. The protocol has been adapted from successful studies of small molecules to address the challenges associated with modeling protein-protein interactions. Specifically, we built homology models of the three antibody-gp120 complexes, extended the sampling times for large bulky residues, incorporated the modeling of glycans on the surface of gp120, and utilized continuum solvent-based loop prediction protocols to improve sampling. We present three experimental surface plasmon resonance data sets, in which antibody residues in the antibody/gp120 interface were systematically mutated to alanine. The RMS error in the large set (55 total cases) of FEP tests as compared to these experiments, 0.68kcal/mol, is near experimental accuracy, and it compares favorably with the results obtained from a simpler, empirical methodology. The correlation coefficient for the combined data set including residues with glycan contacts, R=0.49, should be sufficient to guide the choice of residues for antibody optimization projects, assuming that this level of accuracy can be realized in prospective prediction. More generally, these results are encouraging with regard to the possibility of using an FEP approach to calculate the magnitude of protein-protein binding affinities.
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http://dx.doi.org/10.1016/j.jmb.2016.11.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5383735PMC
April 2017

Accelerating drug discovery through tight integration of expert molecular design and predictive scoring.

Curr Opin Struct Biol 2017 04 9;43:38-44. Epub 2016 Nov 9.

Department of Chemistry, Columbia University, New York, NY 10027, United States. Electronic address:

Modeling protein-ligand interactions has been a central goal of computational chemistry for many years. We here review recent progress toward this goal, and highlight the role free energy calculation methods and computational solvent analysis techniques are now having in drug discovery. We further describe recent use of these methodologies to advance two separate drug discovery programs targeting acetyl-CoA carboxylase and tyrosine kinase 2. These examples suggest that tight integration of sophisticated chemistry teams with state-of-the-art computational methods can dramatically improve the efficiency of small molecule drug discovery.
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http://dx.doi.org/10.1016/j.sbi.2016.10.007DOI Listing
April 2017

Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations.

J Chem Theory Comput 2016 Jun 13;12(6):2990-8. Epub 2016 May 13.

Department of Chemistry, Columbia University , New York, New York 10027, United States.

Ligand docking is a widely used tool for lead discovery and binding mode prediction based drug discovery. The greatest challenges in docking occur when the receptor significantly reorganizes upon small molecule binding, thereby requiring an induced fit docking (IFD) approach in which the receptor is allowed to move in order to bind to the ligand optimally. IFD methods have had some success but suffer from a lack of reliability. Complementing IFD with all-atom molecular dynamics (MD) is a straightforward solution in principle but not in practice due to the severe time scale limitations of MD. Here we introduce a metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein-ligand complexes at a practically useful computational cost. Our strategy allows treating this problem in full atomistic detail and in a computationally efficient manner and enhances the predictive power of IFD methods. We significantly increase the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand-receptor systems. We expect this approach to be of significant value in computationally driven drug design.
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http://dx.doi.org/10.1021/acs.jctc.6b00201DOI Listing
June 2016

WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand-Receptor Docking.

J Med Chem 2016 05 22;59(9):4364-84. Epub 2016 Apr 22.

Department of Chemistry, Columbia University , New York, 3000 Broadway, MC 3110, New York 10036, United States.

We have developed a new methodology for protein-ligand docking and scoring, WScore, incorporating a flexible description of explicit water molecules. The locations and thermodynamics of the waters are derived from a WaterMap molecular dynamics simulation. The water structure is employed to provide an atomic level description of ligand and protein desolvation. WScore also contains a detailed model for localized ligand and protein strain energy and integrates an MM-GBSA scoring component with these terms to assess delocalized strain of the complex. Ensemble docking is used to take into account induced fit effects on the receptor conformation, and protein reorganization free energies are assigned via fitting to experimental data. The performance of the method is evaluated for pose prediction, rank ordering of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complexes and compared with the Glide SP and Glide XP models; significant improvements are obtained.
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http://dx.doi.org/10.1021/acs.jmedchem.6b00131DOI Listing
May 2016

Highly efficient implementation of pseudospectral time-dependent density-functional theory for the calculation of excitation energies of large molecules.

J Comput Chem 2016 06 25;37(16):1425-41. Epub 2016 Mar 25.

Department of Chemistry, Columbia University, New York, New York, 10027.

We have developed and implemented pseudospectral time-dependent density-functional theory (TDDFT) in the quantum mechanics package Jaguar to calculate restricted singlet and restricted triplet, as well as unrestricted excitation energies with either full linear response (FLR) or the Tamm-Dancoff approximation (TDA) with the pseudospectral length scales, pseudospectral atomic corrections, and pseudospectral multigrid strategy included in the implementations to improve the chemical accuracy and to speed the pseudospectral calculations. The calculations based on pseudospectral time-dependent density-functional theory with full linear response (PS-FLR-TDDFT) and within the Tamm-Dancoff approximation (PS-TDA-TDDFT) for G2 set molecules using B3LYP/6-31G*(*) show mean and maximum absolute deviations of 0.0015 eV and 0.0081 eV, 0.0007 eV and 0.0064 eV, 0.0004 eV and 0.0022 eV for restricted singlet excitation energies, restricted triplet excitation energies, and unrestricted excitation energies, respectively; compared with the results calculated from the conventional spectral method. The application of PS-FLR-TDDFT to OLED molecules and organic dyes, as well as the comparisons for results calculated from PS-FLR-TDDFT and best estimations demonstrate that the accuracy of both PS-FLR-TDDFT and PS-TDA-TDDFT. Calculations for a set of medium-sized molecules, including Cn fullerenes and nanotubes, using the B3LYP functional and 6-31G(**) basis set show PS-TDA-TDDFT provides 19- to 34-fold speedups for Cn fullerenes with 450-1470 basis functions, 11- to 32-fold speedups for nanotubes with 660-3180 basis functions, and 9- to 16-fold speedups for organic molecules with 540-1340 basis functions compared to fully analytic calculations without sacrificing chemical accuracy. The calculations on a set of larger molecules, including the antibiotic drug Ramoplanin, the 46-residue crambin protein, fullerenes up to C540 and nanotubes up to 14×(6,6), using the B3LYP functional and 6-31G(**) basis set with up to 8100 basis functions show that PS-FLR-TDDFT CPU time scales as N(2.05) with the number of basis functions. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/jcc.24350DOI Listing
June 2016

Evaluation of the Performance of the B3LYP, PBE0, and M06 DFT Functionals, and DBLOC-Corrected Versions, in the Calculation of Redox Potentials and Spin Splittings for Transition Metal Containing Systems.

J Chem Theory Comput 2016 Mar 5;12(3):1121-8. Epub 2016 Feb 5.

Department of Chemistry, Columbia University , New York, New York 10027, United States.

We have evaluated the performance of the M06 and PBE0 functionals in their ability to calculate spin splittings and redox potentials for octahedral complexes containing a first transition metal series atom. The mean unsigned errors (MUEs) for these two functionals are similar to those obtained for B3LYP using the same data sets. We then apply our localized orbital correction approach for transition metals, DBLOC, in an effort to improve the results obtained with both functionals. The PBE0-DBLOC results are remarkably close in both MUE and parameter values to those obtained for the B3LYP-DBLOC method. The M06-DBLOC results are less accurate, but the parameter values and trends are still qualitatively very similar. These results demonstrate that DBLOC corrected methods are substantially more accurate for these systems than any of the uncorrected functionals we have tested and that the deviations between hybrid DFT methods and experiment for transition metal containing systems exhibit striking physically based regularities which are very similar for the three functionals that we have examined, despite significant differences in the details of each model.
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http://dx.doi.org/10.1021/acs.jctc.5b00782DOI Listing
March 2016

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.

J Chem Theory Comput 2016 Jan 1;12(1):281-96. Epub 2015 Dec 1.

Department of Chemistry, Columbia University , 3000 Broadway, New York, New York 10027, United States.

The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.
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http://dx.doi.org/10.1021/acs.jctc.5b00864DOI Listing
January 2016

Successful application of the DBLOC method to the hydroxylation of camphor by cytochrome p450.

Protein Sci 2016 Jan 15;25(1):277-85. Epub 2015 Dec 15.

Department of Chemistry, Columbia University, New York, New York, 10027.

The activation barrier for the hydroxylation of camphor by cytochrome P450 was computed using a mixed quantum mechanics/molecular mechanics (QM/MM) model of the full protein-ligand system and a fully QM calculation using a cluster model of the active site at the B3LYP/LACVP*/LACV3P** level of theory, which consisted of B3LYP/LACV3P** single point energies computed at B3LYP/LACVP* optimized geometries. From the QM/MM calculation, a barrier height of 17.5 kcal/mol was obtained, while the experimental value was known to be less than or equal to 10 kcal/mol. This process was repeated using the D3 correction for hybrid DFT in order to investigate whether the inadequate treatment of dispersion interaction was responsible for the overestimation of the barrier. While the D3 correction does reduce the computed barrier to 13.3 kcal/mol, it was still in disagreement with experiment. After application of a series of transition metal optimized localized orbital corrections (DBLOC) and without any refitting of parameters, the barrier was further reduced to 10.0 kcal/mol, which was consistent with the experimental results. The DBLOC method to CH bond activation in methane monooxygenase (MMO) was also applied, as a second, independent test. The barrier in MMO was known, by experiment, to be 15.4 kcal/mol. After application of the DBLOC corrections to the MMO barrier compute by B3LYP, in a previous study, and accounting for dispersion with Grimme's D3 method, the unsigned deviation from experiment was improved from 3.2 to 2.3 kcal/mol. These results suggested that the combination of dispersion plus localized orbital corrections could yield significant quantitative improvements in modeling the catalytic chemistry of transition-metal containing enzymes, within the limitations of the statistical errors of the model, which appear to be on the order of approximately 2 kcal/mole.
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http://dx.doi.org/10.1002/pro.2819DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4815313PMC
January 2016

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

J Am Chem Soc 2015 Feb 12;137(7):2695-703. Epub 2015 Feb 12.

Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.
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http://dx.doi.org/10.1021/ja512751qDOI Listing
February 2015

Conformational preferences underlying reduced activity of a thermophilic ribonuclease H.

J Mol Biol 2015 Feb 27;427(4):853-866. Epub 2014 Dec 27.

Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. Electronic address:

The conformational basis for reduced activity of the thermophilic ribonuclease HI enzyme from Thermus thermophilus, compared to its mesophilic homolog from Escherichia coli, is elucidated using a combination of NMR spectroscopy and molecular dynamics (MD) simulations. Explicit-solvent all-atom MD simulations of the two wild-type proteins and an E. coli mutant in which a glycine residue is inserted after position 80 to mimic the T. thermophilus protein reproduce the differences in conformational dynamics determined from (15)N spin-relaxation NMR spectroscopy of three loop regions that surround the active site and contain functionally important residues: the glycine-rich region, the handle region, and the β5/αE loop. Examination of the MD trajectories indicates that the thermophilic protein samples conformations productive for substrate binding and activity less frequently than the mesophilic enzyme, although these differences may manifest as either increased or decreased relative flexibility of the different regions. Additional MD simulations indicate that mutations increasing activity of the T. thermophilus enzyme at mesophilic temperatures do so by reconfiguring the local environments of the mutated sites to more closely resemble active conformations. Taken together, the results show that both locally increased and decreased flexibility contribute to an overall reduction in activity of T. thermophilus ribonuclease H compared to its mesophilic E. coli homolog.
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http://dx.doi.org/10.1016/j.jmb.2014.11.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349505PMC
February 2015
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