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28 papers · ranked by Valyu relevance

01
PPubMed15 Aug 2016
SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity SVM-Prot Update: A Web-Server for Protein Function Prediction Irrespective of Sequence Similarity
Ying Hong Li, Jing Yu Xu, Lin Tao, Xiao Feng Li + 10 more
Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein…
94relevance
02
PPubMed11 Dec 2014
A Survey of Computational Intelligence Techniques in Protein Function Prediction
Arvind Kumar Tiwari, Rajeev Srivastava
During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they…
93relevance
03
AarXiv17 Oct 2017
ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network
Renzhi Cao, Colton Freitas, Leong Chan, Miao Sun + 2 more
'Zhangxin Chen'] With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction…
93relevance
04
PPubMed20 Jun 2024
A comprehensive review and comparison of existing computational methods for protein function prediction
Baohui Lin, Xiaoling Luo, Yumeng Liu, Xiaopeng Jin
Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational…
93relevance
05
BbioRxiv23 Apr 2019
DeepGOPlus: Improved protein function prediction from sequence
Maxat Kulmanov, Robert Hoehndorf
Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein–protein interaction networks…
93relevance
06
PPubMed27 Jul 2023
Current successes and remaining challenges in protein function prediction Jeffery 10.3389/fbinf.2023.1222182
Constance J. Jeffery
In recent years, improvements in protein function prediction methods have led to increased success in annotating protein sequences. However, the functions of over 30% of protein-coding genes remain unknown for many sequenced genomes. Protein functions vary widely, from catalyzing chemical reactions to binding DNA or…
92relevance
07
PPubMed31 Oct 2008
The Rough Guide to In Silico Function Prediction, or How To Use Sequence and Structure Information To Predict Protein Function
Marco Punta, Yanay Ofran, Fran Lewitter
The vast majority of known proteins have not yet been characterized experimentally, and there is very little that is known about their function. New unannotated sequences are added to the databases at a pace that far exceeds the one in which they are annotated in the lab. Computational biology offers tools that can…
92relevance
08
PPubMed29 Sep 2022
PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms Sengupta et al. 10.3389/fgene.2022.969915
Kaustav Sengupta, Sovan Saha, Anup Kumar Halder, Piyali Chatterjee + 3 more
'Mita Nasipuri' 'Subhadip Basu' 'Dariusz Plewczynski'] Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple…
91relevance
09
BbioRxiv7 Jul 2022
Integrating Self-Attention Transformer with Triplet Neural Networks for Protein Gene Ontology Prediction
Yi-Heng Zhu, Chengxin Zhang, Dong-Jun Yu, Yang Zhang
Accurate identification of protein function is critical to elucidate life mechanism and design new drugs. We proposed a novel deep-learning method, ATGO, to predict Gene Ontology (GO) attributes of proteins through a triplet neural-network architecture embedded with pre-trained self-attention transformer models. The…
91relevance
10
BbioRxiv4 Oct 2019
Structure-Based Function Prediction using Graph Convolutional Networks
Vladimir Gligorijevic, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman + 8 more
Recent massive increases in the number of sequences available in public databases challenges current experimental approaches to determining protein function. These methods are limited by both the large scale of these sequences databases and the diversity of protein functions. We present a deep learning Graph…
90relevance
11
AarXiv2023
Function Prediction
Bas Stringer, Annika Jacobsen, Qingzhen Hou, Hans de Ferrante + 5 more
'Olga Ivanova' 'Katharina Waury' 'Jose Gavaldá-García' 'Sanne Abeln' 'K. Anton Feenstra'] | 11 | Function Prediction | 1 | | --- | --- | --- | | | Bas Stringer Annika Jacobsen Qingzhen Hou | | | | Hans de Ferrante Olga Ivanova Katharina Waury | | | | Jose Gavald´a-Garc´ıa Sanne Abeln K. Anton Feenstra | | | | 1…
90relevance
12
PPubMed11 Jul 2023
Special Protein or RNA Molecules Computational Identification
Ren Qi, Quan Zou
The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases. Computational methods can help identify proteins and RNA molecules by analyzing genomic and proteomic data, as well as using…
90relevance
13
AarXiv2020
SMISS: a protein function prediction server by integrating multiple sources
Renzhi Cao, Zhaolong Zhong, Jianlin Cheng
SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network, and…
89relevance
14
BbioRxiv25 Mar 2020
CATH functional families predict protein functional sites
Sayoni Das, Harry M. Scholes, Christine A. Orengo
Identification of functional sites in proteins is essential for functional characterisation, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies…
88relevance
15
AarXiv16 Apr 2019
Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences
Ashish Ranjan, Md Shah Fahad, David Fernández‐Baca, Akshay Deepak + 1 more
'Sudhakar Tripathi'] Abstract—The order of amino acids in a protein sequence enables the protein to acquire a conformation suitable for performing functions, thereby motivating the need to analyse these sequences for predicting functions. Although machine learning based approaches are fast compared to methods using…
88relevance
16
AarXiv2022
A Review of Deep Learning Techniques for Protein Function Prediction
Divyanshu Aggarwal, Yasha Hasija
—Deep Learning and big data have shown tremendous success in bioinformatics and computational biology in recent years; artificial intelligence methods have also significantly contributed in the task of protein function classification. This review paper analyzes the recent developments in approaches for the task of…
87relevance
17
BbioRxiv2 Mar 2018
Towards region-specific propagation of protein functions
Da Chen Emily Koo, Richard Bonneau
Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations…
87relevance
18
AarXiv2019
Protein Classification using Machine Learning and Statistical Techniques: A Comparative Analysis
C.L.P. Gupta, Anand Bihari, Sudhakar Tripathi
In recent era prediction of enzyme class from an unknown protein is one of the challenging tasks in bioinformatics. Day to day the number of proteins is increases as result the prediction of enzyme class gives a new opportunity to bioinformatics scholars. The prime objective of this article is to implement the machine…
84relevance
19
BbioRxiv4 Apr 2019
Computing wide range of protein/peptide features from their sequence and structure
Akshara Pande, Sumeet Patiyal, Anjali Lathwal, Chakit Arora + 11 more
In last three decades, a wide range of protein descriptors/features have been discovered to annotate a protein with high precision. A wide range of features have been integrated in numerous software packages (e.g., PROFEAT, PyBioMed, iFeature, protr, Rcpi, propy) to predict function of a protein. These features are not…
82relevance
20
BbioRxiv21 Jun 2023
Seq-InSite: sequence supersedes structure for protein interaction site prediction
SeyedMohsen Hosseini, G. Brian Golding, Lucian Ilie
Proteins accomplish cellular functions by interacting with each other, which makes the prediction of interaction sites a fundamental problem. Computational prediction of the interaction sites has been studied extensively, with the structure-based programs being the most accurate, while the sequence-based ones being…
76relevance
21
CchemRxiv17 Dec 2025
Protein Language Model Embeddings Distinguish Catalytic from Structural Zinc-Binding Sites with Interpretable Attention Signatures
Authors not listed
Zinc ions serve dual roles in proteins—as catalytic cofactors and as structural elements. Distinguishing these functional classes from sequence alone remains challenging because both share similar coordination geometries. Here we demonstrate that ESM-2 embeddings encode sufficient information to classify catalytic…
74relevance
22
AarXiv12 Sep 2018
DeepProteomics: Protein family classification using Shallow and Deep Networks
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…
71relevance
23
BbioRxiv12 Sep 2018
DeepProteomics: Protein family classification using Shallow and Deep Networks
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…
71relevance
24
CchemRxiv6 Mar 2022
In Silico Characterization of Essential Hypothetical Proteins from Francisella tularensis Schu S4 Strain.
Sheikh Sunzid Ahmed
Francisella tularensis Schu S4 is the causal agent of a sporadic zoonotic disease known as Tularemia, which has shown epidemic outbreaks recently in certain parts of the world. This pathogen is a potential agent of biowarfare or bioterrorism and is classified as a category A pathogen by the National Institute of…
70relevance
25
CchemRxiv23 May 2019
Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction
Fergus Boyles, Charlotte M Deane, Garrett Morris
Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand complex, with…
66relevance
26
CchemRxiv29 Sep 2023
Predicting SARS-CoV-2 Protein Interactions: Insights from Machine Learning
Nihal Dadheech
In our research journey, we undertook a comprehensive exploration of protein-protein interaction (PPI) prediction, with a primary focus on unraveling the intricate web of interactions involving the SARS-CoV-2 virus. Our research endeavor encompassed a multi-faceted approach that seamlessly integrated data…
65relevance
27
CchemRxiv27 Jan 2021
Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses
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…
65relevance
28
CchemRxiv15 Oct 2018
CANDOCK: Chemical Atomic Network Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials
Jonathan Fine, Janez Konc, Ram Samudralal, Gaurav Chopra
Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, ignoring interactions with essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically…
56relevance

Results for protein function prediction

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity SVM-Prot Update: A Web-Server for Protein Function Prediction Irrespective of Sequence Similarity

A Survey of Computational Intelligence Techniques in Protein Function Prediction

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network

A comprehensive review and comparison of existing computational methods for protein function prediction

DeepGOPlus: Improved protein function prediction from sequence

Current successes and remaining challenges in protein function prediction Jeffery 10.3389/fbinf.2023.1222182

The Rough Guide to In Silico Function Prediction, or How To Use Sequence and Structure Information To Predict Protein Function

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms Sengupta et al. 10.3389/fgene.2022.969915

Integrating Self-Attention Transformer with Triplet Neural Networks for Protein Gene Ontology Prediction

Structure-Based Function Prediction using Graph Convolutional Networks

Function Prediction

Special Protein or RNA Molecules Computational Identification

SMISS: a protein function prediction server by integrating multiple sources

CATH functional families predict protein functional sites

Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences

A Review of Deep Learning Techniques for Protein Function Prediction

Towards region-specific propagation of protein functions

Protein Classification using Machine Learning and Statistical Techniques: A Comparative Analysis

Computing wide range of protein/peptide features from their sequence and structure

Seq-InSite: sequence supersedes structure for protein interaction site prediction

Protein Language Model Embeddings Distinguish Catalytic from Structural Zinc-Binding Sites with Interpretable Attention Signatures

DeepProteomics: Protein family classification using Shallow and Deep Networks

DeepProteomics: Protein family classification using Shallow and Deep Networks

In Silico Characterization of Essential Hypothetical Proteins from Francisella tularensis Schu S4 Strain.

Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction

Predicting SARS-CoV-2 Protein Interactions: Insights from Machine Learning

Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses

CANDOCK: Chemical Atomic Network Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity SVM-Prot Update: A Web-Server for Protein Function Prediction Irrespective of Sequence Similarity

A Survey of Computational Intelligence Techniques in Protein Function Prediction

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network

A comprehensive review and comparison of existing computational methods for protein function prediction

DeepGOPlus: Improved protein function prediction from sequence

Current successes and remaining challenges in protein function prediction Jeffery 10.3389/fbinf.2023.1222182

The Rough Guide to In Silico Function Prediction, or How To Use Sequence and Structure Information To Predict Protein Function

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms Sengupta et al. 10.3389/fgene.2022.969915

Integrating Self-Attention Transformer with Triplet Neural Networks for Protein Gene Ontology Prediction

Structure-Based Function Prediction using Graph Convolutional Networks

Function Prediction

Special Protein or RNA Molecules Computational Identification

SMISS: a protein function prediction server by integrating multiple sources

CATH functional families predict protein functional sites

Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences

A Review of Deep Learning Techniques for Protein Function Prediction

Towards region-specific propagation of protein functions

Protein Classification using Machine Learning and Statistical Techniques: A Comparative Analysis

Computing wide range of protein/peptide features from their sequence and structure

Seq-InSite: sequence supersedes structure for protein interaction site prediction

Protein Language Model Embeddings Distinguish Catalytic from Structural Zinc-Binding Sites with Interpretable Attention Signatures

DeepProteomics: Protein family classification using Shallow and Deep Networks

DeepProteomics: Protein family classification using Shallow and Deep Networks

In Silico Characterization of Essential Hypothetical Proteins from Francisella tularensis Schu S4 Strain.

Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction

Predicting SARS-CoV-2 Protein Interactions: Insights from Machine Learning

Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses

CANDOCK: Chemical Atomic Network Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials