Protein structure prediction using bio-inspired algorithm: A review

Karami, Yasaman; Khakzad, Hamed; Shirazi, Hossein; Arab, Seyed Shahriar

doi:10.1109/aisp.2012.6313744

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…Inspired by natural evolution, the genetic algorithm has been applied to design molecules of targeted properties for a variety of tasks [ 164 , 165 ]. An evolutionary algorithm with ML is a popular solution for improving the antimicrobial activity of template sequences [ 166 , 167 ].…”

Section: Amp Design By Optimizationmentioning

confidence: 99%

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

et al. 2022

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Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

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Section: Amp Design By Optimizationmentioning

confidence: 99%

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

et al. 2022

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“…It is worth mentioning other reviews on this area, such as [189,190], that show the same point of view of different algorithms applied to the protein folding problem, although none of them elaborate a review from the High Performance Computing view.…”

Section: Other Nature-inspired Algorithmsmentioning

confidence: 99%

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Llanes¹,

Muñoz²,

Bueno-Crespo³

et al. 2016

CDT

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The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.

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“…Protein yapı tahmininde Karami ve arkadaşları Bal Arısı, Yapay Arı Kolonisi, Parçacık Sürü Optimizasyon ve Klonal Seçilim algoritmalarına ve hibritlerine değinmişlerdir [4]. Benzer bir çalışma Ye tarafından yüksek lisans tezi olarak yapılmıştır.…”

Section: Li̇teratür öZeti̇ (Literature Review)unclassified

“…3. bölümde makalenin teorik alt yapısında önemli bir yer kaplayan proteinler yapı ve model bakımından incelenmiştir. 4. bölümde çalışmalarda kullanılan Yapay Arı Kolonisi Algoritması (ABC) ve önerilen ayrık modeli anlatılmıştır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Ayrik Yapay Ari Koloni̇si̇ Algori̇tmasi İle Protei̇n Yapisi Tahmi̇ni̇

Batbat

Öztürk

2016

BTD

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Özet-Proteinlerin yerine getirdiği görevler üçüncül yapıları olarak adlandırılan doğal durumlarına doğrudan bağlıdır. Günümüzde birçok proteinin birincil yapı olarak adlandırılan dizilimleri bilinmekle beraber işlevlerini belirleyen üçüncül yapıları bilinmemektedir. Ele alınan dizide bulunan amino asitlerin üçüncül yapı oluşumuna yol açan özelliklerinin kullanılarak üçüncül yapılarının tahmin edilmesi proteinlerin gerçek doğasının tam anlaşılmasının önünü açacaktır. Proteinlerin üçüncül yapıları ile ilgili yapılan çalışmalarda en düşük serbest enerji düzeyinin elde edilmesi eğilimi tespit edilmiştir. Serbest enerji düzeylerini hesaplamada, sudan kaçma özelliği yoğun olarak kullanılmaktadır. Bu özelliğe göre sınıflandırılan amino asitleri temel alan HP modeli basitliği ve gerçekçiliği ile dikkat çekmektedir. Çalışmamızda HP modelinin doğadaki protein davranışı incelenerek geliştirilmiş bir türü olan Üç Boyutlu Hidrofobik Polar Yan Zincir modeli kullanılmıştır. Proteinlerin üçüncül yapı modellerinin bulunmasında arıların besin arama davranışlarını modelleyen Yapay Arı Kolonisi Algoritması temelli oluşturulan ayrık bir model önerilmiştir. Benzer çalışmalarda kullanılan başarımların test edildiği örnek dizilerde daha az maliyetle iyi sonuçlar alınmıştır. Abstract-Tasks that proteins perform are directly related to native conditions named their tertiary structure. Although arrays of numerous proteins' primary structure are known, tertiary structures those explain their functions are not well defined. Studies on translation of proteins to their tertiary structure found tendency of provision of minimal free energy level. HP model, used in calculation of free energy levels and based on the amino acids grouped by their hydrophobic characteristics, is standing out with its simplicity and authenticity. In this paper, three-dimensional hydrophobic side chain model that was developed by investigation of the protein behavior of HP model in nature was used. A new discrete model based on Artificial Bee Colony Algorithm that is simulating the foraging behaviors of bees is proposed to form the protein tertiary structures. The success of proposed model is tested on widely used benchmark protein arrays and compared with the results of similar methods from literature.

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Protein structure prediction using bio-inspired algorithm: A review

Cited by 8 publications

References 37 publications

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Ayrik Yapay Ari Koloni̇si̇ Algori̇tmasi İle Protei̇n Yapisi Tahmi̇ni̇

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