Global Minimum Energy Conformation Research Articles

Bilinear optimization of protein structure prediction: An exact approach via AB off-lattice model

Protein structure prediction (PSP) remains a central challenge in computational biology due to its inherent complexity and high dimensionality. While numerous heuristic approaches have appeared in the literature, their success varies. The AB off-lattice model, which characterizes proteins as sequences of A (hydrophobic) and B (hydrophilic) beads, presents a simplified perspective on PSP. This work presents a mathematical optimization-based methodology capitalizing on the off-lattice AB model. Dissecting the inherent non-linearities of the energy landscape of protein folding allowed for formulating the PSP as a bilinear optimization problem. This formulation was achieved by introducing auxiliary variables and constraints that encapsulate the nuanced relationship between the protein’s conformational space and its energy landscape. The proposed bilinear model exhibited notable accuracy in pinpointing the global minimum energy conformations on a benchmark dataset presented by the Protein Data Bank (PDB). Compared to traditional heuristic-based methods, this bilinear approach yielded exact solutions, reducing the likelihood of local minima entrapment. This research highlights the potential of reframing the traditionally non-linear protein structure prediction problem into a bilinear optimization problem through the off-lattice AB model. Such a transformation offers a route toward methodologies that can determine the global solution, challenging current PSP paradigms. Exploration into hybrid models, merging bilinear optimization and heuristic components, might present an avenue for balancing accuracy with computational efficiency.

Biophysical and structural characterization of tetramethrin serum protein complex and its toxicological implications.

Tetramethrin (TMT) is a commonly used insecticide and has a carcinogenic and neurodegenerative effect on humans. The binding mechanism and toxicological implications of TMT to human serum albumin (HSA) were examined in this study employing a combination of biophysical and computational methods indicating moderate binding affinity and potential hepato and renal toxicity. Fluorescence quenching experiments showed that TMT binds to HSA with a moderate affinity, and the binding process was spontaneous and predominantly enthalpy-driven. Circular dichroism spectroscopy revealed that TMT binding did not induce any significant conformational changes in HSA, resulting in no changes in its alpha-helix content. The binding site and modalities of TMT interactions with HSA as computed by molecular docking and molecular dynamics simulations revealed that it binds to Sudlow site II of HSA via hydrophobic interactions through its dimethylcyclopropane carboxylate methyl propanyl group. The structural dynamics of TMT induce proper fit into the binding site creating increased and stabilizing interactions. Additionally, molecular mechanics-Poisson Boltzmann surface area calculations also indicated that non-polar and van der Waals were found to be the major contributors to the high binding free energy of the complex. Quantum mechanics (QM) revealed the conformational energies of the binding confirmation and the degree of deviation from the global minimum energy conformation of TMT. The results of this study provide a comprehensive understanding of the binding mechanism of TMT with HSA, which is important for evaluating the toxicity of this insecticide in humans.

Alteration of Substrate Specificity and Transglucosylation Activity of GH13_31 α-Glucosidase from Bacillus sp. AHU2216 through Site-Directed Mutagenesis of Asn258 on β→α Loop 5.

α-Glucosidase catalyzes the hydrolysis of α-d-glucosides and transglucosylation. Bacillus sp. AHU2216 α-glucosidase (BspAG13_31A), belonging to the glycoside hydrolase family 13 subfamily 31, specifically cleaves α-(1→4)-glucosidic linkages and shows high disaccharide specificity. We showed previously that the maltose moiety of maltotriose (G3) and maltotetraose (G4), covering subsites +1 and +2 of BspAG13_31A, adopts a less stable conformation than the global minimum energy conformation. This unstable d-glucosyl conformation likely arises from steric hindrance by Asn258 on β→α loop 5 of the catalytic (β/α)8-barrel. In this study, Asn258 mutants of BspAG13_31A were enzymatically and structurally analyzed. N258G/P mutations significantly enhanced trisaccharide specificity. The N258P mutation also enhanced the activity toward sucrose and produced erlose from sucrose through transglucosylation. N258G showed a higher specificity to transglucosylation with p-nitrophenyl α-d-glucopyranoside and maltose than the wild type. E256Q/N258G and E258Q/N258P structures in complex with G3 revealed that the maltose moiety of G3 bound at subsites +1 and +2 adopted a relaxed conformation, whereas a less stable conformation was taken in E256Q. This structural difference suggests that stabilizing the G3 conformation enhances trisaccharide specificity. The E256Q/N258G-G3 complex formed an additional hydrogen bond between Met229 and the d-glucose residue of G3 in subsite +2, and this interaction may enhance transglucosylation.

Potential of mean force conformational energy maps for disaccharide linkages of the Burkholderia multivorans exopolysaccharide C1576 in aqueous solution

Potential of Mean Force Ramachandran energy maps in aqueous solution have been prepared for all of the glycosidic linkages found in the C1576 exopolysaccharide from the biofilms of the bacterial species Burkholderia multivorans, a member of the Burkholderia cepacian complex that was isolated from a cystic fibrosis patient. C1576 is a rhamnomannan with a tetrasaccharide repeat unit. In general, for the four linkage types in this polymer, hydration did not produce dramatic changes in the Ramachandran energy surfaces, with the 3-methyl-α-d-rhamnopyranose-(1→3)-α-d-rhamnopyranose case exhibiting the greatest hydration change, with the global minimum energy conformation shifting by more than 80° in ψ. However, hydration did reduce the rigidity of all the linkages, increasing the overall flexibility of this polysaccharide.

Inhibition of the hexamerization of SARS-CoV-2 endoribonuclease and modeling of RNA structures bound to the hexamer

Non-structural protein 15 (Nsp15) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) forms a homo hexamer and functions as an endoribonuclease. Here, we propose that Nsp15 activity may be inhibited by preventing its hexamerization through drug binding. We first explored the stable conformation of the Nsp15 monomer as the global free energy minimum conformation in the free energy landscape using a combination of parallel cascade selection molecular dynamics (PaCS-MD) and the Markov state model (MSM), and found that the Nsp15 monomer forms a more open conformation with larger druggable pockets on the surface. Targeting the pockets with high druggability scores, we conducted ligand docking and identified compounds that tightly bind to the Nsp15 monomer. The top poses with Nsp15 were subjected to binding free energy calculations by dissociation PaCS-MD and MSM (dPaCS-MD/MSM), indicating the stability of the complexes. One of the identified pockets, which is distinctively bound by inosine analogues, may be an alternative binding site to stabilize viral RNA binding and/or an alternative catalytic site. We constructed a stable RNA structure model bound to both UTP and alternative binding sites, providing a reasonable proposed model of the Nsp15/RNA complex.

A critical analysis of computational protein design with sparse residue interaction graphs.

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.

Deterministic Search Methods for Computational Protein Design.

One main challenge in Computational Protein Design (CPD) lies in the exploration of the amino-acid sequence space, while considering, to some extent, side chain flexibility. The exorbitant size of the search space urges for the development of efficient exact deterministic search methods enabling identification of low-energy sequence-conformation models, corresponding either to the global minimum energy conformation (GMEC) or an ensemble of guaranteed near-optimal solutions. In contrast to stochastic local search methods that are not guaranteed to find the GMEC, exact deterministic approaches always identify the GMEC and prove its optimality in finite but exponential worst-case time. After a brief overview on these two classes of methods, we discuss the grounds and merits of four deterministic methods that have been applied to solve CPD problems. These approaches are based either on the Dead-End-Elimination theorem combined with A* algorithm (DEE/A*), on Cost Function Networks algorithms (CFN), on Integer Linear Programming solvers (ILP) or on Markov Random Fields solvers (MRF). The way two of these methods (DEE/A* and CFN) can be used in practice to identify low-energy sequence-conformation models starting from a pairwise decomposed energy matrix is detailed in this review.

Clathrates of novel ethylenediamine derivatives: thermal, X-ray crystallographic and conformational analysis of inclusion complexes of N,N′-bis(5-phenyl-5-dibenzo[a,d]cycloheptenyl)ethylenediamine and its 10,11-dihydro analogue

Novel compounds N,N′-bis(5-phenyl-10,11-dihydro-5-dibenzo[a,d]cycloheptenyl)ethylenediamine 3a and N,N′-bis(5-phenyl-5-dibenzo[a,d]cycloheptenyl)ethylenediamine 3b are efficient hosts, forming clathrates upon recrystallization from a wide variety of organic solvents. The 1:2 and 1:1 complexes of 3a and 3b, respectively, with dichloromethane, dibromomethane and diiodomethane were subjected to extensive thermal and X-ray crystallographic studies, and it was found that guests of the thermally less stable complexes reside in channels, while these are accommodated in discrete cavities in the more stable complexes. Host 3a adopts similar conformations in its complexes with CH2Cl2 and CH2Br2, while a different conformer is involved with CH2I2. DFT (B3LYP/6-31G*) geometry optimizations revealed that these conformers are respectively, 8.7 and 7.5 kJ mol−1 less stable than the global minimum energy conformer of 3a. Host 3b adopts similar conformations in its three complexes, which optimize to the global minimum energy conformer. In the thermally more stable complexes, 3a and 3b exist as relatively low energy conformers.

Computational Protein Design Using AND/OR Branch-and-Bound Search.

The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this article, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many problems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.

COSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design.

Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches.

Cover Image, Volume 37, Issue 12

Journal of Computational ChemistryVolume 37, Issue 12 p. i-i Cover ImageFree Access Cover Image, Volume 37, Issue 12 First published: 08 April 2016 https://doi.org/10.1002/jcc.24366AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract The cover picture highlights the use of cost function networks (CFN) in computational protein design (CPD). These methods provide important speedups to explore large sequence-conformation spaces and provably identify the sequence with the conformation of optimal stability, i.e., the global minimum energy conformation (GMEC). On page 1048 (DOI: 10.1002/jcc.24290), Seydou Traoré et al. show that, in addition to quickly finding the GMEC in highly complex protein design problems, CFN-based methods also enable the efficient enumeration of sub-optimal sequences. These approaches offer an attractive alternative to the usual CPD methods. Volume37, Issue12May 5, 2016Pages i-i RelatedInformation

Adaptive Particle Swarm Optimizer with Varying Acceleration Coefficients for Finding the Most Stable Conformer of Small Molecules.

A novel parameter automation strategy for Particle Swarm Optimization called APSO (Adaptive PSO) is proposed. The algorithm is designed to efficiently control the local search and convergence to the global optimum solution. Parameters c1 controls the impact of the cognitive component on the particle trajectory and c2 controls the impact of the social component. Instead of fixing the value of c1 and c2 , this paper updates the value of these acceleration coefficients by considering time variation of evaluation function along with varying inertia weight factor in PSO. Here the maximum and minimum value of evaluation function is use to gradually decrease and increase the value of c1 and c2 respectively. Molecular energy minimization is one of the most challenging unsolved problems and it can be formulated as a global optimization problem. The aim of the present paper is to investigate the effect of newly developed APSO on the highly complex molecular potential energy function and to check the efficiency of the proposed algorithm to find the global minimum of the function under consideration. The proposed algorithm APSO is therefore applied in two cases: Firstly, for the minimization of a potential energy of small molecules with up to 100 degrees of freedom and finally for finding the global minimum energy conformation of 1,2,3-trichloro-1-flouro-propane molecule based on a realistic potential energy function. The computational results of all the cases show that the proposed method performs significantly better than the other algorithms.

Improved energy bound accuracy enhances the efficiency of continuous protein design.

Flexibility and dynamics are important for protein function and a protein's ability to accommodate amino acid substitutions. However, when computational protein design algorithms search over protein structures, the allowed flexibility is often reduced to a relatively small set of discrete side-chain and backbone conformations. While simplifications in scoring functions and protein flexibility are currently necessary to computationally search the vast protein sequence and conformational space, a rigid representation of a protein causes the search to become brittle and miss low-energy structures. Continuous rotamers more closely represent the allowed movement of a side chain within its torsional well and have been successfully incorporated into the protein design framework to design biomedically relevant protein systems. The use of continuous rotamers in protein design enables algorithms to search a larger conformational space than previously possible, but adds additional complexity to the design search. To design large, complex systems with continuous rotamers, new algorithms are needed to increase the efficiency of the search. We present two methods, PartCR and HOT, that greatly increase the speed and efficiency of protein design with continuous rotamers. These methods specifically target the large errors in energetic terms that are used to bound pairwise energies during the design search. By tightening the energy bounds, additional pruning of the conformation space can be achieved, and the number of conformations that must be enumerated to find the global minimum energy conformation is greatly reduced.

MetREx: a protein design approach for the exploration of sequence-reactivity relationships in metalloenzymes.

Metalloenzymes represent a particular challenge for any rational (re)design approach because the modeling of reaction events at their metallic cofactors requires time-consuming quantum mechanical calculations, which cannot easily be reconciled with the fast, knowledge-based approaches commonly applied in protein design studies. Here, an approach for the exploration of sequence-reactivity relationships in metalloenzymes is presented (MetREx) that consists of force field-based screening of mutants that lie energetically between a wild-type sequence and the global minimum energy conformation and which should, therefore, be compatible with a given protein fold. Mutant candidates are subsequently evaluated with a fast and approximate quantum mechanical/molecular mechanical-like procedure that models the influence of the protein environment on the active site by taking partial charges and van der Waals repulsions into account. The feasibility of the procedure is demonstrated for the active site of [FeFe] hydrogenase from Desulfovibrio desulfuricans. The method described allows for the identification of mutants with altered properties, such as inhibitor-coordination energies, and the understanding of the robustness of enzymatic reaction steps with respect to variations in sequence space.

An efficient parallel algorithm for accelerating computational protein design.

Motivation: Structure-based computational protein design (SCPR) is an important topic in protein engineering. Under the assumption of a rigid backbone and a finite set of discrete conformations of side-chains, various methods have been proposed to address this problem. A popular method is to combine the dead-end elimination (DEE) and A* tree search algorithms, which provably finds the global minimum energy conformation (GMEC) solution.Results: In this article, we improve the efficiency of computing A* heuristic functions for protein design and propose a variant of A* algorithm in which the search process can be performed on a single GPU in a massively parallel fashion. In addition, we make some efforts to address the memory exceeding problem in A* search. As a result, our enhancements can achieve a significant speedup of the A*-based protein design algorithm by four orders of magnitude on large-scale test data through pre-computation and parallelization, while still maintaining an acceptable memory overhead. We also show that our parallel A* search algorithm could be successfully combined with iMinDEE, a state-of-the-art DEE criterion, for rotamer pruning to further improve SCPR with the consideration of continuous side-chain flexibility.Availability: Our software is available and distributed open-source under the GNU Lesser General License Version 2.1 (GNU, February 1999). The source code can be downloaded from http://www.cs.duke.edu/donaldlab/osprey.php or http://iiis.tsinghua.edu.cn/∼compbio/software.html.Contact: zengjy321@tsinghua.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Solvation of Vanadium Cations in Sulfuric and Perfluorosulfonic Acids and Their Effect on the Morphology of PFSA Membranes

The desire, in recent decades, to replace fossil fuel with renewable green energy sources has led to the increasing demand for large scale electrical energy storage devices. Redox flow batteries (RFBs) are among the promising large-scale electrical energy storage systems and have received significant attention due to their superior properties such as long life-span, simple installation, the possibility of instant charging by replacing the electrolytes, and the ability to repeatedly store and convert electrical energy into chemical energy and vice versa. Of the various RFBs currently under investigation, the vanadium redox flow battery (VRFB) demonstrates design flexibility, good electrochemical activity, reversibility, and low maintenance cost [1,2]. A VRFB consists of a V2+/V3+ sulfate solution at the negative electrolyte and a VO2+/VO2 + sulfate solution at the positive electrolyte separated by a proton exchange membrane (PEM). The wide span between the standard reduction potentials of the two half-cells in these batteries produces a standard voltage of 1.25 V, which is comparable to other types of RFBs [3].Of the many issues that hinder the commercialization of these storage devices, the poor stability of VO2 + solution at high temperatures and high concentrations, results in precipitation of hydrated V2O5 and energy loss [4]. Although, the stability of VO2 + ion may be increased by the addition of hydrochloric acid, eventually it will form a gel type precipitate involving the V3+ cation [5]. It has also been found that the low ion selectivity of PEMs (mostly perfluorosulfonic acid (PFSA) membranes) allows permeation of vanadium cations and cross contamination of the electrolytes [6,7]. Hence, it is important to understand the solvation of vanadium cations in both the electrolyte solution and PFSA membrane. Knowing the nature of the solvated cation in electrolyte can help to understand the stability of the electrolyte solution as well as the formation of other ionic complexes. Also, the study of the diffused cations within PFSA can help in designing membranes with higher ion selectivity, distinguishing the proton from vanadium cations.In this work the hydration structure of all four vanadium cations in the sulfuric acid environment is examined with ab initio electronic structure calculations. The introduction of a sulfuric acid molecule to each of the hydrated vanadium ions was performed beginning from different initial positions for the acid molecule with respect to the aqueous complex, and determining a global minimum energy conformation for each system. Since the dissociation of the acid molecule depends on its concentration and pH of the solution, the hydration structures were examined in the presence of SO4 2ˉ, HSO4ˉ, and H2SO4. The results indicate that the sulfuric acid in both dissociated and un-dissociated form can affect the first hydration of vanadium cations compared to their bulk water structure [8]. Figure 1 shows the global minimum structures obtained for the hydrated V3+ in presence of sulfuric acid, and the bisulfate and sulfate ions.To study the solvation of vanadium cations in perfluorosulfonic acid systems, electronic structure calculations were performed in the presence of trifluoromethanesulfonic (triflic) acid and it’s dissociated (triflate) anion. The introduction of triflic acid or triflate to each system was performed using the same protocol used for the sulfuric acid. It has been found that similar to sulfuric acid case the triflic acid can also affect the first hydration of vanadium cations. However, none of the resulting global minimums had shown a strong covalent bonding indicating the presence of weak interaction between the diffused vanadium ions and sulfonic acid.The above results were used in the morphology prediction of a PFSA membrane used in VRFBs, assisting in the definition of the coarse-grained species. The coarse-grained simulations performed using dissipative particle dynamics simulations to study the effect of the diffusion of different vanadium cations through hydrated Nafion. The morphology of the hydrated Nafion, two dimensional contour plots of species densities, the average radius of the water domains and average diffusivity of the different species were determined.

A new framework for computational protein design through cost function network optimization

The main challenge for structure-based computational protein design (CPD) remains the combinatorial nature of the search space. Even in its simplest fixed-backbone formulation, CPD encompasses a computationally difficult NP-hard problem that prevents the exact exploration of complex systems defining large sequence-conformation spaces. We present here a CPD framework, based on cost function network (CFN) solving, a recent exact combinatorial optimization technique, to efficiently handle highly complex combinatorial spaces encountered in various protein design problems. We show that the CFN-based approach is able to solve optimality a variety of complex designs that could often not be solved using a usual CPD-dedicated tool or state-of-the-art exact operations research tools. Beyond the identification of the optimal solution, the global minimum-energy conformation, the CFN-based method is also able to quickly enumerate large ensembles of suboptimal solutions of interest to rationally build experimental enzyme mutant libraries. The combined pipeline used to generate energetic models (based on a patched version of the open source solver Osprey 2.0), the conversion to CFN models (based on Perl scripts) and CFN solving (based on the open source solver toulbar2) are all available at http://genoweb.toulouse.inra.fr/~tschiex/CPD

Dead‐end elimination with perturbations (DEEPer): A provable protein design algorithm with continuous sidechain and backbone flexibility

Computational protein and drug design generally require accurate modeling of protein conformations. This modeling typically starts with an experimentally determined protein structure and considers possible conformational changes due to mutations or new ligands. The DEE/A* algorithm provably finds the global minimum-energy conformation (GMEC) of a protein assuming that the backbone does not move and the sidechains take on conformations from a set of discrete, experimentally observed conformations called rotamers. DEE/A* can efficiently find the overall GMEC for exponentially many mutant sequences. Previous improvements to DEE/A* include modeling ensembles of sidechain conformations and either continuous sidechain or backbone flexibility. We present a new algorithm, DEEPer (Dead-End Elimination with Perturbations), that combines these advantages and can also handle much more extensive backbone flexibility and backbone ensembles. DEEPer provably finds the GMEC or, if desired by the user, all conformations and sequences within a specified energy window of the GMEC. It includes the new abilities to handle arbitrarily large backbone perturbations and to generate ensembles of backbone conformations. It also incorporates the shear, an experimentally observed local backbone motion never before used in design. Additionally, we derive a new method to accelerate DEE/A*-based calculations, indirect pruning, that is particularly useful for DEEPer. In 67 benchmark tests on 64 proteins, DEEPer consistently identified lower-energy conformations than previous methods did, indicating more accurate modeling. Additional tests demonstrated its ability to incorporate larger, experimentally observed backbone conformational changes and to model realistic conformational ensembles. These capabilities provide significant advantages for modeling protein mutations and protein-ligand interactions.

A Robust Force Field Based Method for Calculating Conformational Energies of Charged Drug-Like Molecules

The binding affinity of a drug-like molecule depends among other things on the availability of the bioactive conformation. If the bioactive conformation has a significantly higher energy than the global minimum energy conformation, then the molecule is unlikely to bind to its target. Determination of the global minimum energy conformation and calculation of conformational penalties of binding is a prerequisite for prediction of reliable binding affinities. Here, we present a simple and computationally efficient procedure to estimate the global energy minimum for a wide variety of structurally diverse molecules, including polar and charged compounds. Identifying global energy minimum conformations of such compounds with force field methods is problematic due to the exaggeration of intramolecular electrostatic interactions. We demonstrate that the global energy minimum conformations of zwitterionic compounds generated by conformational analysis with modified electrostatics are good approximations of the conformational distributions predicted by experimental data and with molecular dynamics performed in explicit solvent. Finally the method is used to calculate conformational penalties for zwitterionic GluA2 agonists and to filter false positives from a docking study.

Inclusion complexes of buckycatcher with C60 and C70

The dispersion corrected B97-D functional studies find a previously overlooked conformer of the buckycatcher C(60)H(28) (2) exhibiting intramolecular pi-pi stacking of its corannulene pincers to represent a global potential energy minimum conformation. B97-D/TZVP calculated geometry of C(60)@2 supramolecular assembly is in excellent agreement with the X-ray structure, slightly better than the previously reported M06-L results. In contrast, our calculated binding energy of C(60)@2 complex is dramatically higher than both M05-2X and M06-2X results and we conclude that the latter numbers are grossly underestimated. The lack of specificity of the buckycatcher for molecular recognition of C(60)vs. C(70) fullerenes is predicted by our calculations and confirmed by the NMR titration experiment which provides the equilibrium constant of 6800 +/- 400 M(-1) for association of C(70) with C(60)H(28) which is only slightly lower than the previously reported association constant of C(60)@2 assembly.