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Evaluation of methods to cap molecular fragments in calculating energies of interaction in avian pancreatic polypeptide.

ABSTRACT: The accuracy of the determination of the energy of interaction between Phe20 and the Pro5-Thr6-Tyr7-Pro8 complex inside the hydrophobic core of avian pancreatic polypeptide was investigated using three capping strategies for molecular fractionation with conjugated caps and DFT quantum chemical calculations at the BHandHLYP/cc-pVTZ level of theory. The most accurate determination resulted from acetylation of the alpha-amino group combined with methyl amidation of the alpha-carbonyl group with relative deviations less than 10%. Combinations of hydrogenation of the alpha-amino group with the replacement of the alpha-carbonyl group with a hydrogen and the hydrogenation of the alpha-amino group with methylation of the alpha-carbonyl group were less accurate, leading to relative deviations up to 35%. Choice of capping methods depends on the structural features of the polypeptide system, the desired accuracy and the available computational resources.

SUBMITTER: Hatfield MP

PROVIDER: S-EPMC2577377 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Evaluation of methods to cap molecular fragments in calculating energies of interaction in avian pancreatic polypeptide.

Hatfield Marcus P D MP Palermo Nicholas Y NY Csontos József J Murphy Richard F RF Lovas Sándor S

International journal of quantum chemistry 20080101 5

The accuracy of the determination of the energy of interaction between Phe20 and the Pro5-Thr6-Tyr7-Pro8 complex inside the hydrophobic core of avian pancreatic polypeptide was investigated using three capping strategies for molecular fractionation with conjugated caps and DFT quantum chemical calculations at the BHandHLYP/cc-pVTZ level of theory. The most accurate determination resulted from acetylation of the alpha-amino group combined with methyl amidation of the alpha-carbonyl group with rel ...[more]

PMID: 18985167

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Project description:Recent advances in local electron correlation approaches have enabled the relatively routine access to CCSD(T) [that is, coupled cluster (CC) with single, double, and perturbative triple excitations] computations for molecules of a hundred or more atoms. Here, approaching their complete basis set (CBS) limit becomes more challenging due to extensive basis set superposition errors, often necessitating the use of large atomic orbital (AO) basis sets with diffuse functions. Here, we study a potential remedy in the form of non-atom-centered or floating orbitals (FOs). FOs are still rarely employed even for small molecules due to the practical complication of defining their position, number, exponents, etc. The most frequently used FO method thus simply places a single FO center with a large number of FOs toward the middle of noncovalent dimers; however, a single FO center for larger complexes can soon become insufficient. A recent alternative uses a grid of FO centers around the monomers with a single s function per center, which is currently applicable only for H, C, N, and O atoms. Here, we build on the above advantages and mitigate some drawbacks of previous FO approaches by using a layer of FO centers and 4-9 FOs/center for each monomer. Thus, a double layer of FOs is placed between the interacting subsystems. When extending the double-ζ AO basis with this double layer of FOs, the quality of conventional augmented double-ζ or conventional triple-ζ AO bases can be reached or surpassed with less orbitals, leading to few tenths of a kcal/mol basis set errors for medium-sized dimers. This good performance extends to larger molecules (shown here up to 72 atoms), as efficient local natural orbital (LNO) CCSD(T) computations with only double-ζ AO and 4 FOs/center FO bases match our LNO-CCSD(T)/CBS reference within ca. 0.1 kcal/mol. These developments introduce FO methods to the accurate modeling of large molecular complexes without limitations to atom types by further accelerating efficient correlation calculations, like LNO-CCSD(T).

Project description:BackgroundSymptom clusters are conventionally distilled into a single score using composite scoring, which is based on the mathematical assumption that all symptoms are equivalently related to outcomes of interest; this may lead to a loss of important variation in the data.ObjectivesThis article compares two ways of calculating a single score for a symptom cluster: a conventional, hypothesis-driven composite score versus a data-driven, reduced rank regression score that weights the symptoms based on their individual relationships with key outcomes.MethodsWe conducted a secondary analysis of psychoneurological symptoms from a sample of 356 low-income mothers. Four of the psychoneurological symptoms (fatigue, cognitive dysfunction, sleep disturbance, and depressed mood) were measured with the Center for Epidemiological Studies Depression Scale; the fifth (pain) was measured using an item from the Medical Outcomes Study 12-item Short Form Health Survey (SF-12). Mothers' function was measured using the 12-item Short Form Health Survey. The composite score was calculated by summing standardized scores for each individual psychoneurological symptom. In contrast, reduced rank regression weighted the individual symptoms using their respective associations with mothers' function; the weighted individual symptom scores were summed into the reduced rank regression symptom score.ResultsThe composite score and reduced rank regression score were highly correlated at .93. The cluster of psychoneurological symptoms accounted for 53.7% of the variation in the mothers' function. Depressed mood and pain accounted for almost all the explained variation in mothers' function at 37.2% and 15.0%, respectively.DiscussionThe composite score approach was simpler to calculate, and the high correlation with the reduced rank regression score indicates that the composite score reflected most of the variation explained by the reduced rank regression approach in this data set. However, the reduced rank regression analysis provided additional information by identifying pain and depressed mood as having the strongest association with a mother's function, which has implications for understanding which symptoms to target in future interventions. Future studies should also explore composite versus reduced rank regression approaches given that reduced rank regression may yield different insights in other data sets.

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Evaluation of methods to cap molecular fragments in calculating energies of interaction in avian pancreatic polypeptide.

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Evaluation of methods to cap molecular fragments in calculating energies of interaction in avian pancreatic polypeptide.

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