25 papers · ranked by Valyu relevance
Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green + 29 more
'Michal Zielinski' 'Augustin Žídek' 'Alex Bridgland' 'Andrew Cowie' 'Clemens Meyer' 'Agata Laydon' 'Sameer Velankar' 'Gerard J. Kleywegt' 'Alex Bateman' 'Richard Evans' 'Alexander Pritzel' 'Michael Figurnov' 'Olaf Ronneberger' 'Russ Bates' 'Simon A. A. Kohl' 'Anna Potapenko' 'Andrew J. Ballard' 'Bernardino…
Alexey Drozdetskiy, Christian Cole, James Procter, Geoffrey J. Barton
'Geoffrey J. Barton'] JPred4 ([http://www.compbio.dundee.ac.uk/jpred4]()) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary…
Letícia M. F. Bertoline, Angélica N. Lima, Jose E. Krieger, Samantha K. Teixeira
'Samantha K. Teixeira'] Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is…
Jianlin Cheng, Jilong Li, Zheng Wang, Jesse Eickholt + 1 more
Background As genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental…
Sanne Abeln, Jaap Heringa, K. Anton Feenstra
This chapter gives a graceful introduction to problem of protein threedimensional structure prediction, and focuses on how to make structural sense out of a single input sequence with unknown structure, the 'query' or 'target' sequence. We give an overview of the different classes of modelling techniques, notably…
Larry Bliss, Ben Pascoe, Samuel K Sheppard
Protein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on…
Xinru Qiu, Han Li, Greg Ver Steeg, Adam Godzik + 1 more
Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of…
Bin Huang, Lupeng Kong, Chao Wang, Fusong Ju + 7 more
Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and…
Aleeza Kazmi, Muhammad Kazim, Faisal Aslam, Syeda Mahreen-ul-Hassan Kazmi + 6 more
Protein is the building block for all organisms. Protein structure prediction is always a complicated task in the field of proteomics. DNA and protein databases can find the primary sequence of the peptide chain and even similar sequences in different proteins. Mainly, there are two methodologies based on the presence…
Lingtao Chen, Qiaomu Li, Kazi Fahim Ahmad Nasif, Ying Xie + 7 more
'Bobin Deng' 'Shuteng Niu' 'Seyedamin Pouriyeh' 'Zhiyu Dai' 'Jiawei Chen' 'Chloe Yixin Xie' 'Shuai Cheng Li'] Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It…
Yajie Meng, Zhuang Zhang, Chang Zhou, Xianfang Tang + 4 more
'Geng Tian' 'Jialiang Yang' 'Yuhua Yao'] The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental…
Mahmood A. Rashid, Firas Khatib, Abdul Sattar
—Protein structure prediction is a challenging and unsolved problem in computer science. Proteins are the sequence of amino acids connected together by single peptide bond. The combinations of the twenty primary amino acids are the constituents of all proteins. In-vitro laboratory methods used in this problem are very…
Juami H. M. van Gils, Maurits Dijkstra, Punto Bawono, Jose Gavaldá-García + 6 more
'Jose Gavaldá-García' 'Mascha Okounev' 'Robbin Bouwmeester' 'Bas Stringer' 'Jaap Heringa' 'Sanne Abeln' 'K. Anton Feenstra'] | 9 Structural Property Prediction | | 1 | | --- | --- | --- | | Maurits Dijkstra Katharina Waury | Dea Gogishvili | | | Punto Bawono @ Isabel Houtkamp | Jose Gavald´a-Garc´ıa | | | Mascha…
Konstantinos Kalogeropoulos, Markus-Frederik Bohn, David E. Jenkins, Jann Ledergerber + 8 more
Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold.…
Shuichiro Makigaki, Takashi Ishida
Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from…
Surbhi Dhingra, Ramanathan Sowdhamini, Frédéric Cadet, Bernard Offmann
'Bernard Offmann'] Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in…
Igor Nelson
Diverse methods have been proposed for protein secondary structure prediction. However, such task still presents a challenge in bioinformatics. In this article various of these methods are implemented and analysed. First, a baseline using Support Vector Machine. Then a convolutional neural network (CNN), a Long…
Authors not listed
Structure elucidation --- determining molecular structures from spectroscopic data -- remains one of chemistry's most fundamental and challenging tasks, essential for advancing fields from drug discovery to materials science. While machine learning approaches have attempted to automate this process, they typically…
Victor Daniel Aldas-Bulos, Fabien Plisson
Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles. Nevertheless, they are primarily trained on sequences where the tridimensional structures of peptides and proteins are often overlooked. We need a fast and reliable approach to…
Leila Khalatbari, Mohammad Reza Kangavari, Saeid Hosseini, Hongzhi Yin + 1 more
'Hongzhi Yin' 'Ngai‐Man Cheung'] Abstract. The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightly connected to its specific 3D…
Moruf A. Adeagbo, Valdete M. Gonçalves-Almeida, Sandro C. Izidoro, Sabrina A. Silveira
Protein–protein interactions (PPIs) play a central role in elucidating cellular mechanisms. However, a substantial gap remains in current prediction models, as they frequently overlook the structural and physicochemical context governing molecular binding, thereby limiting predictive accuracy. To address this…
Somesh Mohapatra, Daniel Griffin
Identification and control of impurities play a critical role in chemical process development for drug substance synthesis. Most chemical reactions result in a number of by-products and side-products, along with the intended major product. While chemists can predict many of the main process impurities, it remains…
Authors not listed
The rapid advancements in computational methods have revolutionized drug discovery and development. These methods, ranging from molecular modelling to machine learning algorithms, have drastically increased in number and sophistication. However, a comprehensive understanding of these diverse approaches is essential for…
Ashar J. Malik, Chandra S. Verma, Anthony M. Poole, Jane R. Allison
Protein structures carry signal of common ancestry and can therefore aid in reconstructing their evolutionary histories. To expedite the structure-informed inference process, a web server, Structome, has been developed, that allows users to rapidly identify protein structures similar to a query protein and to assemble…
Fergus Boyles, Charlotte M Deane, Garrett Morris
Machine learning scoring functions for protein-ligand binding affinity have been found to consistently outperform classical scoring functions when trained and tested on crystal structures of bound protein-ligand complexes. However, it is less clear how these methods perform when applied to docked poses of complexes. We…