Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

被引:0
|
作者
Claussen, Erin R. [1 ]
Renfrew, P. Douglas [2 ]
Mueller, Christian L. [3 ,4 ,5 ]
Drew, Kevin [1 ]
机构
[1] Univ Illinois, Dept Biol Sci, Chicago, IL 60607 USA
[2] Flatiron Inst, Ctr Computat Biol, New York, NY USA
[3] Ludwig Maximilians Univ Munchen, Dept Stat, Munich, Germany
[4] Helmholtz Zentrum Munchen, Inst Computat Biol, Munich, Germany
[5] Flatiron Inst, Ctr Computat Math, New York, NY USA
基金
美国国家卫生研究院;
关键词
CMA-ES; covariance matrix adaptation evolution strategy; derivative-free optimization; hotspot residues; peptidomimetic; Rosetta; scaffold matcher; PROTEIN-PROTEIN INTERACTIONS; COMPUTATIONAL DESIGN; OPTIMIZATION; PREDICTION; INHIBITORS; PEPTIDES; BINDING;
D O I
10.1002/prot.26619
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms ' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.
引用
收藏
页码:343 / 355
页数:13
相关论文
共 50 条
  • [1] The lens design using the CMA-ES algorithm
    Nagata, Y
    GENETIC AND EVOLUTIONARY COMPUTATION GECCO 2004 , PT 2, PROCEEDINGS, 2004, 3103 : 1189 - 1200
  • [2] Conformal spherical array pattern synthesis based on CMA-ES Algorithm
    Zhang, Z. P.
    Gu, P. F.
    Wang, G.
    Ding, D. Z.
    Chen, R. S.
    2020 IEEE MTT-S INTERNATIONAL CONFERENCE ON NUMERICAL ELECTROMAGNETIC AND MULTIPHYSICS MODELING AND OPTIMIZATION (NEMO 2020), 2020,
  • [3] Identifying Idealised Vectors for Emotion Detection Using CMA-ES
    Alshahrani, Mohammed
    Samothrakis, Spyridon
    Fasli, Maria
    PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION), 2019, : 157 - 158
  • [4] Niche radius adaptation in the CMA-ES niching algorithm
    Shir, Ofer M.
    Back, Thomas
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN IX, PROCEEDINGS, 2006, 4193 : 142 - 151
  • [5] Optimum Coordination of Overcurrent Relays Using CMA-ES Algorithm
    Singh, Manohar
    Panigrahi, B.
    Mukherjee, Rohan
    IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, DRIVES AND ENERGY SYSTEMS (PEDES 2012), 2012,
  • [6] A CMA-ES Algorithm Allowing for Random Parameters in Model Calibration
    Sauerland, Volkmar
    von Hallern, Claudine
    Kriest, Iris
    Getzlaff, Julia
    JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 2023, 15 (08)
  • [7] Per Instance Algorithm Configuration of CMA-ES with Limited Budget
    Belkhir, Nacim
    Dreo, Johann
    Saveant, Pierre
    Schoenauer, Marc
    PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17), 2017, : 681 - 688
  • [8] An efficient conjugate gradient based Cholesky CMA-ES estimation algorithm for nonlinear systems
    Mao, Yawen
    Xu, Chen
    Chen, Jing
    Pu, Yan
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2024, 34 (03) : 1610 - 1628
  • [9] On Equivalence of Algorithm's Implementations: The CMA-ES Algorithm and Its Five Implementations
    Biedrzycki, Rafal
    PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION), 2019, : 247 - 248
  • [10] Coordination of Electro-Mechanical Based Overcurrent Relays using CMA-ES Algorithm
    Singh, Manohar
    2016 IEEE 7TH POWER INDIA INTERNATIONAL CONFERENCE (PIICON), 2016,