ALPHAFOLD: DERİN ÖĞRENME VE SİNİR AĞLARI YOLUYLA PROTEİN KATLAMASINDA DEVRİM YARATMAK

Year 2023, , 445 - 466, 31.12.2023

Burcu Tekin , Rafig Gurbanov

https://doi.org/10.55071/ticaretfbd.1323165

Abstract

AlphaFold, bir protein dizisinin üç boyutlu yapısını tahmin etmek için derin sinir ağlarını ve gelişmiş makine öğrenimi tekniklerini kullanan, DeepMind ekibi tarafından geliştirilmiş bir protein katlama tahmin aracıdır. Protein katlanmasının tahmini, hesaplamalı biyolojide uzun süredir devam eden bir sorun olmuştur ve doğru protein yapısı tahmin yöntemlerinin geliştirilmesi, bilim camiasının büyük ilgisini çekmiştir. AlphaFold, önce bir proteinin yerel yapısını tahmin ettiği ve ardından genel yapıyı bir araya getirdiği iki aşamalı bir yaklaşım kullanır. AlphaFold, iki yılda bir yapılan CASP (Yapı Tahmininin Kritik Değerlendirmesi) deneylerinde diğer son teknoloji yöntemleri geride bırakarak çok çeşitli proteinlerin yapısını tahmin etmede kayda değer bir başarı elde etmiştir. AlphaFold'un tahminlerinin doğruluğu, protein işlevini ve hastalık mekanizmalarını, ilaç keşfini ve sentetik biyolojiyi anlamak için önemli etkilere sahiptir. Bu derlemede, AlphaFold'un geliştirilmesine, temel metodolojisine ve CASP deneylerindeki performansına genel bir bakış sunulmaktadır. Ek olarak, AlphaFold'un protein mühendisliği, ilaç keşfi ve yapısal biyolojideki potansiyel uygulamaları da tartışılmaktadır.

Keywords

Protein yapı tahmini, Protein katlanması, AlphaFold, Makine öğrenmesi

ALPHAFOLD: REVOLUTIONIZING PROTEIN FOLDING THROUGH DEEP LEARNING AND NEURAL NETWORKS

Abstract

AlphaFold is a protein folding prediction tool developed by the DeepMind team, which leverages deep neural networks and advanced machine learning techniques to predict the three-dimensional structure of a protein sequence. The prediction of protein folding has been a long-standing challenge in computational biology, and the development of accurate protein structure prediction methods has been of great interest to the scientific community. AlphaFold employs a two-stage approach, in which it first predicts the local structure of a protein and then assembles the global configuration. AlphaFold has achieved remarkable success in predicting the structure of a wide range of proteins, outperforming other state-of-the-art methods in the biennial CASP (Critical Assessment of Structure Prediction) experiments. The accuracy of AlphaFold's predictions has significant implications for understanding protein function and disease mechanisms, drug discovery, and synthetic biology. This review provides an overview of AlphaFold's development, basic methodology, and performance in CASP experiments. Moreover, potential applications of AlphaFold in protein engineering, drug discovery, and structural biology are also discussed.

Keywords

Protein structure prediction, Protein folding, AlphaFold, Machine learning

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