protein function

Annotating genomes with DeepGO protein function prediction tools

Year: 2025 Venue: Protein Function Prediction Authors: Rund Tawfiq, Kexin Niu, Maxat Kulmanov, Robert Hoehndorf DOI: 10.1007/978-1-0716-4662-5_10 Abstract This chapter explores the evolution of DeepGO, a suite of deep learning-based tools for protein function prediction, in the form of Gene Ontology (GO) terms, and their applications in genome annotation. We provide a comprehensive overview of the different versions of DeepGO, highlighting key advancements introduced by each method. To demonstrate the practical application of these tools, we present a case study on the annotation of a

Computational prediction of protein functional annotations

Year: 2025 Venue: Protein Function Prediction Authors: Maxat Kulmanov, Robert Hoehndorf DOI: 10.1007/978-1-0716-4662-5_1 Abstract Protein function prediction is a crucial task in bioinformatics and computational biology, as it enables the understanding of disease mechanisms, development of new therapeutics, and improvement of crop yields. Despite significant advances, the majority of protein functions remain unknown or poorly annotated, hindering our understanding of biological systems. This review provides a comprehensive overview of the available methods for protein function prediction

Exploring Gene Ontology Annotations with OWL

Year: 2011 Venue: Proceedings of the 13th Bio-Ontology Meeting Authors: Simon Jupp, Robert Stevens, Robert Hoehndorf Abstract Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other activities. Tools, such as AmiGO, allow exploration of genes based on their GO annotations. This human driven exploration and querying of GO is obviously useful, but by taking advantage of the ontological representation we can use these annotations to create a rich polyhierarchy of gene products for enhanced querying

LLM Agent Based Protein Function Prediction

Year: 2025 Venue: Biocomputing 2026 Authors: Fernando Zhapa-Camacho, Olga Mashkova, Robert Hoehndorf, Maxat Kulmanov DOI: 10.1142/9789819824755_0036 Abstract Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies are expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a graph out of ontologies, neglecting the semantics of the logic therein. Recent

Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL

Venue: Journal of Biomedical Semantics Authors: Simon Jupp, Robert Stevens, Robert Hoehndorf Abstract MOTIVATION:Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products

Lattice-Based ALC Ontology Embeddings With Saturation

Year: 2025 Venue: Neurosymbolic Artificial Intelligence Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1177/29498732251340186 Abstract Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies are expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a graph out of ontologies, neglecting the semantics of the logic therein. Recent semantic

DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction

Year: 2024 Venue: Scientific Reports Authors: Rund Tawfiq, Kexin Niu, Robert Hoehndorf, Maxat Kulmanov DOI: 10.1038/s41598-024-82956-w Abstract AbstractAnalyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have

DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms

Year: 2022 Venue: Bioinformatics Authors: Maxat Kulmanov, Robert Hoehndorf DOI: 10.1093/bioinformatics/btac256 Abstract Supplementary data are available at Bioinformatics online. Topics Protein function · Neuro-symbolic AI

DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web

Year: 2021 Venue: Nucleic Acids Research Authors: Maxat Kulmanov, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1093/nar/gkab373 Abstract Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

Year: 2017 Venue: Bioinformatics Authors: Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf Abstract Motivation: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Year: 2019 Venue: Genome Biology Authors: Naihui Zhou, Yuxiang Jiang, Timothy R Bergquist, Alexandra J Lee, Balint Z Kacsoh, Alex W Crocker, Kimberley A Lewis, George Georghiou, Huy N Nguyen, Md Nafiz Hamid, Larry Davis, Tunca Dogan, Volkan Atalay, Ahmet S Rifaioglu, Alperen Dalkiran, Rengul Cetin-Atalay, Chengxin Zhang, Rebecca L Hurto, Peter L Freddolino, Yang Zhang, Prajwal Bhat, Fran Supek, Jos\'e M Fern\'andez, Branislava Gemovic, Vladimir R Perovic, Radoslav S Davidovi\'c, Neven Sumonja, Nevena Veljkovic, Ehsaneddin Asgari, Mohammad RK Mofrad, Giuseppe Profiti, Castrense Savojardo, Pier

Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

Year: 2016 Venue: PLoS ONE Authors: Imane Boudellioua, Rabie Saidi, Robert Hoehndorf, Maria J. Martin, Victor Solovyev DOI: 10.1371/journal.pone.0158896 Abstract The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein

DeepGOPlus: improved protein function prediction from sequence

Year: 2020 Venue: Bioinformatics Authors: Maxat Kulmanov, Robert Hoehndorf Abstract 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, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the

Predicting protein functions using positive-unlabeled ranking with ontology-based priors

Year: 2024 Venue: Bioinformatics Authors: Fernando Zhapa-Camacho, Zhenwei Tang, Maxat Kulmanov, Robert Hoehndorf DOI: 10.1093/bioinformatics/btae237 Abstract Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive

Protein function

Determining what a protein does, from its sequence alone, is one of the foundational problems of computational molecular biology. Experimental characterisation cannot keep pace with sequencing throughput, and large-scale ontologies such as the Gene Ontology (GO) provide the structured background knowledge needed to make automated assignment of function tractable. Our work centres on the DeepGO family of systems, which couple deep neural networks with the formal axioms of the Gene Ontology so that predicted annotations are not only accurate but also logically consistent with what is already

Genomic Context

Bacterial protein-function prediction that exploits operon and genome-neighbourhood structure in addition to sequence and homology. Get it GitHub: https://github.com/bio-ontology-research-group/Genomic_context ★ 3 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

PhenoGoCon

Predicts gene–phenotype associations from predicted Gene Ontology functions; bridges GO function prediction and HPO/MPO phenotype prediction. Get it GitHub: https://github.com/bio-ontology-research-group/phenogocon ★ 3 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

GO-Agent

LLM-agent framework that decomposes protein-function prediction into tool-calling sub-tasks (sequence search, structure lookup, domain reasoning) and stitches the evidence into a final GO annotation. Get it GitHub: https://github.com/bio-ontology-research-group/go-agent ★ 7 Developed in projects KAUST Center of Excellence for Generative AI (Health and Wellness, BCB theme) Category: Protein Function Prediction

DeepPheno

Predicts loss-of-function organism-level phenotypes (HPO/MPO) directly from a gene's annotated functions, using a hierarchical neural classifier over phenotype ontologies. Get it GitHub: https://github.com/bio-ontology-research-group/deeppheno ★ 6 Developed in projects CompleX: Variant Prioritization in Complex Disease Category: Protein Function Prediction

PU-GO

Positive-unlabeled ranking of protein functions with ontology-based priors; directly addresses the partial-annotation problem in CAFA benchmarks. Get it GitHub: https://github.com/bio-ontology-research-group/PU-GO ★ 4 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

DeepGOMeta

DeepGO trained specifically for metagenomic communities; predicts functional roles of proteins recovered from environmental samples and links them to biogeochemical processes. Get it GitHub: https://github.com/bio-ontology-research-group/deepgometa ★ 8 Developed in projects Enabling desert revegetation by AI-tailored soil microbiome fortification Enabling mangrove restoration by AI-tailored microbiome fortification Category: Protein Function Prediction

DeepGOZero

Zero-shot extension of DeepGO using model-theoretic ELEmbeddings to predict GO classes that have never been observed during training. Get it GitHub: https://github.com/bio-ontology-research-group/deepgozero ★ 34 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

DeepGO2

Next-generation DeepGO model with transformer protein embeddings and improved hierarchical multi-label prediction. Get it GitHub: https://github.com/bio-ontology-research-group/deepgo2 ★ 57 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

DeepGO

Original sequence-based, ontology-aware deep classifier for predicting Gene Ontology functional annotations; basis of the entire DeepGO family of tools. Get it GitHub: https://github.com/bio-ontology-research-group/deepgo ★ 87 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Bio2Vec: Smart analytics infrastructure for the life sciences Category: Protein Function Prediction

DeepGOPlus

CNN-ensemble protein-function predictor that augments sequence-based scoring with k-nearest-neighbour homology and GO axioms; one of the strongest CAFA-evaluated open models. Get it GitHub: https://github.com/bio-ontology-research-group/deepgoplus ★ 103 Homepage: https://deepgo.cbrc.kaust.edu.sa/deepgo/ Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Bio2Vec: Smart analytics infrastructure for the life sciences Category: Protein Function Prediction

Melissa Rios Zertuche

Melissa Maria Rios Zertuche completed her MSc in Bioscience at KAUST in 2024 under the supervision of Robert Hoehndorf. Her thesis project was carried out in collaboration with the Eppinger lab and developed a computational pipeline for the rational design and optimisation of nanobodies. Her thesis, Establishment and Evaluation of a Computational Workflow for the Design and Optimization of Nanobodies, addresses the lack of comprehensive end-to-end computational tools tailored to the design of single-domain antibodies. Nanobodies (VHHs) are roughly one-tenth the size of conventional antibodies

Md Nurul Muttakin

Md Nurul Muttakin completed his MSc in Computer Science at KAUST in 2023 under the supervision of Robert Hoehndorf. His research focused on machine learning methods for predicting protein function from three-dimensional structural information. His thesis, 3D conformation-based protein function prediction, investigated how structural information should be exploited by deep learning models in light of the availability of near-experimental-accuracy structures from AlphaFold. Almost all existing structure-aware protein function predictors combine graph neural networks (GNNs) operating on 2D

Amal Alhelal

Amal Alhelal completed her MSc in Bioengineering at KAUST in 2024 under the supervision of Robert Hoehndorf. She was a student in the Bioinformatics and Machine Learning track of the Bioengineering programme, based at the Computational Bioscience Research Center (CBRC), and defended her thesis in May 2024. Her thesis, titled Protein functional domain identification methodology (defended under the working title Exploring the association between protein functional domains and pathogenicity information derived from AI tools), investigated computational approaches to delineate functional domains

Application of AI in Bioinformatics (2025)

Year: 2025 Code: CS 321 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Neuro-symbolic AI, Protein function

Algorithms in Bioinformatics (2020)

Year: 2020 Code: CS 290 Program: Computer Science / Bioengineering Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Algorithms in Bioinformatics (2022)

Year: 2022 Code: CS 249 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Algorithms in Bioinformatics (2025)

Year: 2025 Code: CS 249 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function
Computational methods for functional metagenomics: from protein functions to multi-scale interactions — project cover

Computational methods for functional metagenomics: from protein functions to multi-scale interactions

Metagenomic sequencing has made it routine to read the DNA of an entire microbial community, but most analysis pipelines stop at taxonomic composition or at the level of individual protein families. The really biologically informative questions, which proteins do what, which proteins interact, which metabolic pathways are reconstructible, and how the community as a whole interacts with its environment or host, remain largely out of reach computationally. Even associations that are very robust empirically, for example between gut microbiome composition and colorectal cancer or inflammatory
Evolutionary potential of corals to adapt to climate warming — project cover

Evolutionary potential of corals to adapt to climate warming

Reef-building corals in the Red Sea are exposed to among the highest sea-surface temperatures of any reef system on Earth, yet survive and reproduce at conditions that bleach corals elsewhere. Understanding the molecular basis of this resilience, and the limits of acclimatization and adaptation as the Red Sea continues to warm, is one of the central questions in marine climate biology. The project combines reciprocal transplant experiments along the north-south Red Sea thermal gradient with genome-resolved transcriptomics, epigenomics, and host-symbiont metabarcoding to dissect how the coral
Enabling desert revegetation by AI-tailored soil microbiome fortification — project cover

Enabling desert revegetation by AI-tailored soil microbiome fortification

Roughly twelve million hectares of dryland are lost every year worldwide, and the megaprojects launched to reverse desertification — the Sahel's Great Green Wall is the canonical example — typically fail because most planted trees die when irrigation stops. The biological reason is that desert soils are low in nutrients and high in salinity, and the microbial communities that mediate nitrogen, phosphate, and mineral acquisition for plants are missing or unbalanced. This project, led by Heribert Hirt's plant-microbiome group with our group and the Modelling and Simulation group of Gabriel

Meet KAUST prospective student: Juexiao Zhou

3 min read ·
Juexiao Zhou is a 21-year-old graduate from Shenzhen, China, who will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate and member of the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.