MSEED: A program for the rapid analytical determination of accessible surface areas and their derivatives

Perrot, Georges; Cheng, Betty H. C.; Gibson, Kenneth D.; Vila, Jorge A.; Palmer, Kathleen A.; Nayeem, Akbar; Maigret, Bernard; Scheraga, Harold A.

doi:10.1002/jcc.540130102

Cited by 155 publications

(112 citation statements)

References 30 publications

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“…Surface areas were calculated with the Connolly algorithm using a dot density of 10 A -2 (Connolly, 1983). In more recent implementations of PDA-SETUP, the MSEED algorithm of Scheraga has been used in conjunction with the Connolly algorithm to speed up the calculation (Perrot et al, 1992).…”

Section: Sequence Optimization: Dee and Monte Carlo Searchmentioning

confidence: 99%

Protein design automation

1996

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We have conceived and implemented a cyclical protein design strategy that couples theory, computation, and experimental testing. The combinatorially large number of possible sequences and the incomplete understanding of the factors that control protein structure are the primary obstacles in protein design. Our protein design automation algorithm objectively predicts protein sequences likely to achieve a desired fold. Using a rotamer description of the side chains, we implemented a fast discrete search algorithm based on the Dead-End Elimination Theorem to rapidly find the globally optimal sequence in its optimal geometry from the vast number of possible solutions. Rotamer sequences were scored for steric complementarity using a van der Waals potential. A Monte Carlo search was then executed, starting at the optimal sequence, in order to find other high-scoring sequences. As a test of the design methodology, high-scoring sequences were found for the buried hydrophobic residues of a homodimeric coiled coil based on GCN4-p1. The corresponding peptides were synthesized and characterized by CD spectroscopy and size-exclusion chromatography. All peptides were dimeric and nearly 100% helical at 1 "C, with melting temperatures ranging from 24 "C to 57 "C. A quantitative structure activity relation analysis was performed on the designed peptides, and a significant correlation was found with surface area burial. Incorporation of a buried surface area potential in the scoring of sequences greatly improved the correlation between predicted and measured stabilities and demonstrated experimental feedback in a complete design cycle.

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Section: Sequence Optimization: Dee and Monte Carlo Searchmentioning

confidence: 99%

Protein design automation

1996

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“…Существующие в настоящее время алгоритмы построения и исследования поверхности макромолекул можно разделить на две основные группы -аналитические методы [9][10][11][12] и представление поверхности точками [13][14].…”

Section: типы поверхностей белков и методы их построенияunclassified

Development of Mathematical Methods and Software for Protein's Functional Sites Detection

Шутова¹

2010

Math.Biol.Bioinf.

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Аннотация. Разработан программный комплекс, предназначенный для анализа структуры поверхностей белков с целью выявления функциональных сайтов связывания белков. Комплекс позволяет осуществлять построение поверхностей белков, поиск структурных особенностей построенных поверхностей (карманов, каналов и полостей) и исследование найденных структурных особенностей. С помощью разработанного комплекса определен один из функциональных сайтов гранулоцитарного колониестимулирующего фактора человека и спрогнозирован его низкомолекулярный пептидный функциональный аналог. Ключевые слова: функциональный сайт белка, поверхность белка, структурные особенности поверхности белка, карманы, каналы, полости. ВВЕДЕНИЕПоиск потенциальных сайтов связывания белков -актуальнейшая задача биохимии, решение которой лежит в ее новой, быстро развивающейся области -биоинформатике. Успешное предсказание участков связывания белков с их лигандами является предпосылкой для решения ряда других не менее актуальных задач биохимии, к которым можно отнести прогноз белок-белковых и белок-ДНК взаимодействий, выявление функциональных сайтов белков с известными функциями и предсказание функциональных свойств новых белков, структуры которых получены, в частности, в ходе выполнения геномных проектов. Каждая из перечисленных задач лежит в основе решения разнообразных прикладных и практически значимых подзадач, связанных, в первую очередь, с целенаправленным конструированием соединений с заданной биологической активностью. Такой рациональный подход к созданию соединений с заданными свойствами позволяет существенно снизить как временные, так и материальные затраты на создание, в частности, новых лекарственных препаратов.Так как функциональные свойства белков определяются их конформацией, то есть расположением полипептидной цепи в пространстве [1], то поиск сайтов связывания белков логично основывать на анализе и исследовании их пространственной структуры. Развитие физико-химических методов, позволяющих экспериментально определять пространственную организацию макромолекул, привело к появлению банков и баз с биохимическими данными и размещению в них расшифрованных пространственных структур как белков, так и их комплексов с лигандами различной природы. Накопление большого объема этих биохимических данных дало импульс к * shutova2001@mail.ru

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“…As in article I, the energy was minimized by the L-BFGS procedure, 80 the solvent-accessible surface area (SASA) and its first derivatives were calculated by the program MSEED 81 for both the loop and the template, and the conformational search was carried out with the LTD method, 52,31 where all these programs have been incorporated within TINKER. For calculating the SASA, a water molecule is represented by a sphere of radius 1.4 Å, and the radius r i of atom i, is determined from its LennardJones parameter i (LJ), where r i ϭ 2 1/6 i (LJ)/2; the radius of a hydrogen is 0.9 Å.…”

Section: Loops Studied and Modeling Issuesmentioning

confidence: 99%

Solvation parameters for predicting the structure of surface loops in proteins: Transferability and entropic effects

Das

Meirovitch

2003

Proteins

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A new procedure for optimizing parameters of implicit solvation models introduced by us has been applied successfully first to cyclic peptides and more recently to three surface loops of ribonuclease A (Das and Meirovitch, Proteins 2001; 43:303-314) using the simplified model E tot ‫؍‬ E FF ( ‫؍‬ nr) ؉ ⌺ i i A i , where i are atomic solvation parameters (ASPs) to be optimized, A i is the solvent accessible surface area of atom i, E FF ( ‫؍‬ nr) is the AMBER force-field energy of the loop-loop and looptemplate interactions with a distance-dependent dielectric constant, ‫؍‬ nr, where n is a parameter. The loop is free to move while the protein template is held fixed in its X-ray structure; an extensive conformational search for energy minimized loop structures is carried out with our local torsional deformation method. The optimal ASPs and n are those for which the structure with the lowest minimized energy [E tot (n, i )] becomes the experimental X-ray structure, or less strictly, the energy gap between these structures is within 2-3 kcal/mol. To check if a set of ASPs can be defined, which is transferable to a large number of loops, we optimize individual sets of ASPs (based on n ‫؍‬ 2) for 12 surface loops from which an "averaged" best-fit set is defined. This set is then applied to the 12 loops and an independent "test" group of 8 loops leading in most cases to very small RMSD values; thus, this set can be useful for structure prediction of loops in homology modeling. For three loops we also calculate the free energy gaps to find that they are only slightly smaller than their energy counterparts, indicating that only larger n will enable reducing too large gaps. Because of its simplicity, this model allowed carrying out an extensive application of our methodology, providing thereby a large number of benchmark results for comparison with future calculations based on n > 2 as well as on more sophisticated solvation models with as yet unknown performance for loops. Proteins 2003;51:470 -483.

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MSEED: A program for the rapid analytical determination of accessible surface areas and their derivatives

Cited by 155 publications

References 30 publications

Protein design automation

Protein design automation

Development of Mathematical Methods and Software for Protein's Functional Sites Detection

Solvation parameters for predicting the structure of surface loops in proteins: Transferability and entropic effects

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