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Search · four archives
28 papers · ranked by Valyu relevance
Marcin J Mizianty, Lukasz Kurgan
Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class…
Suri Dipannita Sayeed, Jan Niclas Wolf, Ina Koch, Guang Song
Protein fold classification reveals key structural information about proteins that is essential for understanding their function. While numerous approaches exist in the literature that classifies protein fold from sequence data using machine learning, there is hardly any approach that classifies protein fold from the…
Antonina Andreeva, Alexey G. Murzin
This review article surveys the protein structures determined by Joint Center for Structural Genomics and published in this special issue of Acta Crystallographica Section F.
Boryeu Mao
In this study, the distributions of protein structure classes (or folding types) of experimentally determined structures from a legacy dataset and a comprehensive database (SCOP) are modeled precisely with geometric constructs such as convex polytopes in high-dimensional amino acid composition space. This is a…
Jad Abbass, Jean-Christophe Nebel
Background Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many…
Yechan Hong, Yongyu Deng, Haofan Cui, Jan Segert + 1 more
The fold classification of a protein reveals valuable information about its shape and function. It is important to find a mapping between protein structures and their folds. There are numerous machine learning techniques to predict protein folds from 1-dimensional (1D) protein sequences, but there are few machine…
Rhiannon Morris, Katrina A. Black, Elliott J. Stollar
Almost all interactions and reactions that occur in living organisms involve proteins. The various biological roles of proteins include, but are not limited to, signal transduction, gene transcription, cell death, immune function, structural support, and catalysis of all the chemical reactions that enable organisms to…
Vladimir R. Rudnev, Liudmila I. Kulikova, Kirill S. Nikolsky, Kristina A. Malsagova + 3 more
'Kristina A. Malsagova' 'Arthur T. Kopylov' 'Anna L. Kaysheva' 'Antonio Rosato'] Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information…
Shohei Konno, Takao Namiki, Koichiro Ishimori
To quantitatively categorize protein structures, we developed a quantitative coarse-grained model of protein structures with a novel amino acid network, the interaction selective network (ISN), characterized by the links based on interactions in both the main and side chains. We found that the ISN is a novel robust…
Menuka Jaiswal, Saad Saleem, Yonghyeon Kweon, Eli J. Draizen + 3 more
'Stella Veretnik' 'Cameron Mura' 'Philip E. Bourne'] Abstract—Recent computational advances in the accurate prediction of protein three-dimensional (3D) structures from amino acid sequences now present a unique opportunity to decipher the interrelationships between proteins. This task entailsbut is not equivalent toa…
Ravi Gupta, Ankush Mittal, Kuldip Singh
This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a…
Rodrigo A. Moreira, Roisin Braddell, Fernando A. N. Santos, Tamàs Fülöp + 3 more
Protein structure analysis and classification, which is fundamental for predicting protein function, still poses formidable challenges in the fields of molecular biology, mathematics, physics and computer science. In the present work we exploit recent advances in computational topology to define a new intrinsic…
Annika Jacobsen, Erik van Dijk, Halima Mouhib, Bas Stringer + 5 more
The the tertiary structure of the protein, which represents its complete structure, consists of secondary structure elements, or motifs (see also Panel "Secondary structure motifs"). The arrangement of secondary structure elements [_page_18_Figure_1.jpeg]: The four main protein fold classes, here showing a more or less…
Jian-Jun Shu, Kian Yan Yong
The correct prediction of protein secondary structures is one of the key issues in predicting the correct protein folded shape, which is used for determining gene function. Existing methods make use of amino acids properties as indices to classify protein secondary structures, but are faced with a significant number of…
Anu Vazhayil, R Vinayakumar, KP Soman
The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes. Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation. The laboratory experiments take a considerable amount of time for annotation of the…
Nathan Labiosa, Aryan Kohli, Christian Chung, Christopher Korban
This study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic…
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…
R. Dustin Schaeffer, Jing Zhang, Kirill E. Medvedev, Qian Cong + 1 more
Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for…
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…
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…
Seyed Ziaeddin Alborzi, Marie-Dominique Devignes, David W. Ritchie
With the growing number of protein structures in the protein data bank (PDB), there is a need to annotate these structures at the domain level in order to relate protein structure to protein function. Thanks to the SIFTS database, many PDB chains are now cross-referenced with Pfam domains and enzyme commission (EC)…
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…
Andre Then, Karel Mácha, Bashar Ibrahim, Stefan Schuster
The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing…
Stephanie Wankowicz, James Fraser
In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting…
Daniel Probst
Last year, a preprint gained notoriety, proposing that a k-nearest neighbour classifier is able to outperform large-language models using compressed text as input and normalised compression distance (NCD) as a metric. In chemistry and biochemistry, molecules are often represented as strings, such as SMILES for small…
Jan Weinreich, Daniel Probst
In recent years, natural language processing approaches to machine learning, most prominently deep neural network-based transformers, have been extensively applied to molecular classification and regression tasks, including the prediction of pharmacokinetic and quantum-chemical properties. However, models based on deep…
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…
Bárbara I. Díaz-Eufracio, José L. Medina-Franco
Protein-protein interaction (PPI) inhibitors have a continued and increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms are able to classify or identify PPI inhibitors. In this work, we describe the performance of different algorithms broadly used in chemoinformatics to develop a…