Conformational searching using a population‐based incremental learning algorithm

Long, S.M.; Tran, Tran Trung; Adams, Peter; Darwen, Paul J.; Smythe, Mark L.

doi:10.1002/jcc.21732

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Computationally designed peptide scaffolds based on this concept are beginning to enter the clinic. PG-200, an orally available interleukin-23 receptor antagonist [59] from Protagonist Therapeutics created from the computational selection of disulfide stabilized peptide scaffolds [60–62], is entering Phase I clinical trials for Crohn’s disease.…”

Section: The Scope Of Automated Protein Designmentioning

confidence: 99%

Recent advances in automated protein design and its future challenges

Setiawan

Brender

Zhang

2018

Expert Opinion on Drug Discovery

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Introduction Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

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Section: The Scope Of Automated Protein Designmentioning

confidence: 99%

Recent advances in automated protein design and its future challenges

Setiawan

Brender

Zhang

2018

Expert Opinion on Drug Discovery

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“…In principle, this demands the knowledge of (1) all stable conformations and (2) the corresponding energies. To address the first problem, one can either systematically count the conformers based on the stable torsional states [1,[9][10][11][12] or use random conformational searches such as MonteCarlo methods [1,13,14], molecular dynamics methods [2,13,15] or even genetic algorithms [16,17]. The case of jet-cooled alkanes allows for favorable approximations and the main spectral contributors can be counted in a simple fashion.…”

Section: N-alkane Conformationsmentioning

confidence: 99%

Unbranched n-Alkanes

Lüttschwager

2014

Raman Spectroscopy of Conformational Rearrangements at Low Temperatures

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In this chapter, the Raman spectroscopic investigation of n-alkane self-solvation will be presented. To support the later analysis, the first section discusses the conformational isomerism of n-alkanes in general and addresses the question to which temperature alkanes must be cooled to work out low-energy conformations. Section 4.2 outlines the vibrational degrees of freedom of unbranched n-alkanes to facilitate the assignment of Raman jet spectra. In Sect. 4.3, the simulation of Raman jet spectra which aid the assignment and provide conformational temperatures is discussed. Raman jet spectra are presented and assigned subsequently in Sect. 4.4. The chapter closes with the determination of the critical folding chain length (n c ) based on the experimental findings and a discussion of computational predictions.

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“…8 Variations of the genetic algorithm have been implemented in various published works to predict molecular structures. 9, 10 Brasil used an evolutionary strategy to solve this problem 11 and Cai and Shao used a hybrid method of EA combined with SA for structure prediction. 12 ParadisEO approached the docking problem with a parallel GA in order to leverage additional processing power and improve its performance and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Haptic-driven, interactive drug design: implementing a GPU-based approach to evaluate the induced fit effect

et al. 2014

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In this paper, we describe a hybrid meta-heuristics method of energy minimization and conformational sampling and its application into our haptic-driven molecular modelling simulator. The proposed method has been designed to suit real-time molecular docking simulations, where the time-lapse between two successive ligand poses is relatively small. In these situations, the energy minimization problem becomes increasingly complex and chaotic. The algorithm is tuned to take advantage of recent advances in GPU computing with asynchronous kernel execution, which has allowed us to include full protein flexibility in the real-time interactive, haptic-driven simulations. Finally, in this paper, we will also discuss the implementation of such high-performance computing approaches in our software, discussing the results of our initial validation studies, highlighting the advantages and limitations of such interactive methodology.

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Conformational searching using a population‐based incremental learning algorithm

Cited by 7 publications

References 28 publications

Recent advances in automated protein design and its future challenges

Recent advances in automated protein design and its future challenges

Unbranched n-Alkanes

Haptic-driven, interactive drug design: implementing a GPU-based approach to evaluate the induced fit effect

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