Popular Reports / computational-biology

---

This report is written by MaltSci based on the latest literature and research findings

---

How does machine learning predict protein function?

Abstract

The prediction of protein functions is a critical challenge in bioinformatics, essential for advancing our understanding of biological systems. Traditionally reliant on experimental techniques, this field has seen a paradigm shift with the integration of machine learning (ML) methodologies. This review explores various ML approaches, including supervised, unsupervised, and deep learning techniques, that utilize large datasets of protein sequences, structures, and interactions to predict protein functions more efficiently and accurately. Notably, deep learning models have demonstrated exceptional capabilities in translating complex biological data into functional predictions, outperforming traditional models. Key methodologies discussed include sequence-based predictions, structural analyses, and the exploration of protein-protein interaction networks. Despite the advancements, challenges such as data quality, model interpretability, and the need for generalization to novel proteins remain significant barriers. Future directions in this field include the integration of multi-omics data and the development of novel algorithms that can harness the complexity of biological information. The implications of these advancements for drug discovery and personalized medicine are profound, emphasizing the transformative potential of computational techniques in enhancing our understanding of proteomics and facilitating therapeutic innovations.

Outline

This report will discuss the following questions.

• 1 Introduction
• 2 Overview of Protein Functions
  • 2.1 Definition and Importance of Protein Functions
  • 2.2 Traditional Methods for Protein Function Prediction
• 3 Machine Learning Approaches in Protein Function Prediction
  • 3.1 Supervised Learning Techniques
  • 3.2 Unsupervised Learning Techniques
  • 3.3 Deep Learning Applications
• 4 Data Sources and Feature Extraction
  • 4.1 Sequence Data
  • 4.2 Structural Data
  • 4.3 Interaction Networks
• 5 Challenges and Limitations
  • 5.1 Data Quality and Availability
  • 5.2 Model Interpretability
  • 5.3 Generalization to Novel Proteins
• 6 Future Directions and Applications
  • 6.1 Integration of Multi-Omics Data
  • 6.2 Enhancements in Model Development
  • 6.3 Implications for Drug Discovery and Personalized Medicine
• 7 Conclusion

1 Introduction

The prediction of protein functions has long been a pivotal challenge in the field of bioinformatics, essential for advancing our understanding of biological systems and processes. Proteins, as the workhorses of cellular activities, perform a myriad of functions that are fundamental to life, ranging from catalyzing biochemical reactions to providing structural support. Traditionally, the elucidation of protein functions relied heavily on experimental techniques, such as biochemical assays and structural biology methods. These approaches, while invaluable, are often time-consuming, expensive, and limited by the availability of suitable experimental conditions. Consequently, there is an increasing demand for computational methods that can predict protein functions more efficiently and accurately, thereby facilitating biological research and therapeutic developments.

The integration of machine learning (ML) into bioinformatics has transformed the landscape of protein function prediction. By leveraging large datasets of protein sequences, structures, and interactions, ML algorithms can uncover patterns and relationships that may not be immediately apparent through traditional analytical methods. Recent advancements in deep learning, a subset of ML, have particularly enhanced the predictive capabilities in this domain, allowing for the processing of complex biological data with unprecedented speed and accuracy [1][2]. This evolution marks a significant shift from heuristic approaches to data-driven methodologies, enabling researchers to generate hypotheses and insights that can guide experimental investigations [3].

Current research in protein function prediction utilizing machine learning encompasses a variety of methodologies, including supervised and unsupervised learning techniques, as well as deep learning applications. Supervised learning approaches typically rely on labeled datasets to train models that can predict functions based on known protein features [4]. In contrast, unsupervised learning methods explore the underlying structure of data without predefined labels, offering a complementary perspective that can reveal novel functional classifications [5]. Deep learning models, such as convolutional neural networks (CNNs), have shown remarkable success in translating protein sequences into functional predictions, demonstrating the potential of these techniques to outperform traditional models [6][7].

This report is structured to provide a comprehensive overview of the methodologies and algorithms employed in machine learning for protein function prediction. Following this introduction, Section 2 will delve into the definition and importance of protein functions, alongside a discussion of traditional methods for their prediction. Section 3 will focus on the various machine learning approaches, highlighting the nuances of supervised, unsupervised, and deep learning techniques. In Section 4, we will explore the critical data sources and feature extraction methods that underpin these predictive models, including sequence data, structural data, and interaction networks. Section 5 will address the challenges and limitations faced in this field, such as data quality and model interpretability, which remain significant barriers to widespread adoption. Finally, Section 6 will outline future directions and applications of machine learning in protein function prediction, emphasizing the integration of multi-omics data and the implications for drug discovery and personalized medicine.

By synthesizing recent advancements and applications of machine learning in predicting protein functions, this report aims to illuminate the transformative potential of computational techniques in enhancing our understanding of proteomics. The insights gleaned from this review will not only underscore the significance of integrating ML with biological data but also highlight the profound implications these predictive models hold for advancing biomedical research and therapeutic innovation.

2 Overview of Protein Functions

2.1 Definition and Importance of Protein Functions

Machine learning (ML) has become an essential tool in predicting protein function, significantly enhancing our understanding of the biological roles of proteins. Proteins are fundamental structural and functional components of living organisms, involved in virtually all cellular processes. The ability to accurately predict protein functions is critical for drug discovery, understanding disease mechanisms, and advancing biotechnology.

The definition of protein function encompasses the various biochemical activities and roles that proteins perform within a cell. These functions are often dictated by the protein's structure, which is determined by its amino acid sequence. However, the traditional experimental methods for determining protein functions are often time-consuming and resource-intensive, leading to a backlog in the annotation of newly sequenced genomes. As a result, computational methods, particularly those leveraging machine learning, have gained prominence.

Machine learning approaches for predicting protein function typically involve several strategies, including sequence-based predictions, structural predictions, and analysis of protein-protein interaction (PPI) networks. The systematic review by Yan et al. (2023) highlights that the rapid growth in protein sequence data has necessitated the development of computational methods for function prediction, with machine learning emerging as a leading approach due to its ability to handle large datasets effectively [4].

In terms of specific methodologies, ML models are trained on known protein functions and sequences to identify patterns and relationships. For instance, Avery et al. (2022) describe how ML has been integrated into computational models to improve prediction accuracy, encompassing applications such as protein structure prediction, protein engineering, and molecular docking [8]. These models can analyze various data types, including sequence, structure, and interaction data, to generate hypotheses about protein functions.

Deep learning, a subset of machine learning, has shown particularly promising results in protein function prediction. Boadu et al. (2025) provide an in-depth review of recent developments in deep learning methods, noting their effectiveness in predicting protein functions from sequence and structural data, which is crucial for generating biological hypotheses [1]. The ability of deep learning models to learn complex representations of data allows for improved predictions, particularly in cases where traditional methods may falter.

Moreover, machine learning facilitates the exploration of protein-ligand interactions, which are vital for understanding drug-target relationships. Vural and Jololian (2025) discuss how ML approaches are employed to predict protein-ligand binding sites, a key aspect of drug discovery [9]. By accurately identifying these binding sites, researchers can better design therapeutics that interact with specific proteins, enhancing the efficacy of drug development.

In conclusion, machine learning plays a pivotal role in predicting protein functions by utilizing vast amounts of biological data to identify patterns and relationships that may not be evident through traditional experimental methods. The integration of ML into protein function prediction not only accelerates the annotation of protein functions but also holds the potential to transform drug discovery and biotechnological applications, ultimately contributing to advancements in health and medicine.

2.2 Traditional Methods for Protein Function Prediction

Machine learning (ML) has emerged as a powerful tool for predicting protein functions, particularly in the context of the growing number of known protein sequences due to advancements in high-throughput sequencing techniques. Traditional methods for protein function prediction often rely on sequence similarity and experimental techniques, which can be labor-intensive and time-consuming. In contrast, ML approaches leverage computational techniques to analyze vast datasets, providing a more efficient means of predicting protein functions.

The core of ML-based protein function prediction lies in its ability to analyze and interpret complex biological data. For instance, a systematic review highlights that traditional experimental techniques are increasingly inadequate for the rapid annotation of protein sequences, prompting the rise of computational methods, particularly those based on ML[4]. These methods encompass various strategies, including the use of sequence data, structural information, protein-protein interaction (PPI) networks, and the fusion of multiple information sources to enhance prediction accuracy.

One significant advancement in this field is the development of models that utilize inter-relationships between protein functions. For example, a study proposed a machine learning-based approach that employs inter-relationships to reduce redundancy among highly correlated functions, thereby improving the model's predictability[10]. The model employs statistical measures such as Pearson's correlation coefficient and Jaccard similarity coefficient to eliminate redundant functions and was tested on datasets like DeepGO and CAFA3, yielding promising results.

Moreover, ML techniques have evolved to incorporate deep learning methodologies, which allow for more sophisticated analyses of protein sequences and structures. A multimodal model was proposed that integrates protein sequence and structural information using Graph Convolutional Networks (GCN), Convolutional Neural Networks (CNN), and Transformer models. This model demonstrated superior performance in predicting molecular functions, biological processes, and cellular components compared to traditional single-modal models[11].

Despite the advancements, challenges remain in the effective prediction of protein functions. Many ML models have been shown to excel in sampling the sequence space to identify deleterious mutations but may not significantly improve the scoring of resulting mutations compared to established methods like Rosetta[12]. This indicates that while ML can enhance certain aspects of protein function prediction, it often complements rather than replaces traditional biophysical methods.

In summary, machine learning predicts protein functions by leveraging vast datasets and sophisticated algorithms to analyze complex biological information. The integration of various data types and the use of advanced ML techniques, such as deep learning, provide a promising avenue for improving the accuracy and efficiency of protein function predictions, addressing the limitations of traditional methods. As the field continues to evolve, the incorporation of diverse datasets and the development of novel algorithms will likely lead to further breakthroughs in understanding protein functions and their implications in biological systems.

3 Machine Learning Approaches in Protein Function Prediction

3.1 Supervised Learning Techniques

Machine learning (ML) has emerged as a transformative approach in predicting protein functions, particularly through supervised learning techniques. These methods leverage experimental data to infer the relationship between protein sequences and their corresponding functions, allowing for efficient exploration of sequence space and optimization of protein properties.

Supervised learning in protein function prediction involves creating models that learn from labeled datasets, where each protein sequence is associated with its known function. By utilizing various algorithms, these models can identify patterns and relationships within the data. For instance, Yang et al. (2019) describe how machine-learning-guided directed evolution can optimize protein functions by predicting how sequence variations correspond to functional changes, effectively accelerating the evolution process without requiring detailed models of biological pathways [13].

Recent advancements have also introduced innovative sequence representation strategies, enhancing data efficiency. For example, Freschlin et al. (2022) highlight the significance of predictive sequence-function models that enable protein engineers to search for useful proteins across vast sequence spaces [14]. This capability is particularly important as traditional experimental techniques often fall short in handling the rapid growth of protein sequence data.

Moreover, deep learning techniques have gained traction within supervised learning frameworks. For instance, Gelman et al. (2021) present a supervised deep learning approach that learns the complex mapping from protein sequence to function using deep mutational scanning data. Their work emphasizes the effectiveness of neural networks in capturing nonlinear interactions, which are crucial for accurately predicting how sequence alterations impact protein behavior [15].

Another significant aspect of supervised learning in protein function prediction is the use of autoencoders, as demonstrated by Dhanuka et al. (2022). Their semi-supervised approach employs multiple autoencoders to classify protein sequences into specific functions based on reconstruction losses, showcasing a promising method for addressing the challenges of function prediction amidst the vast number of available protein sequences [16].

Overall, supervised learning techniques in machine learning facilitate the prediction of protein functions by leveraging experimental data to model the intricate relationships between protein sequences and their functions. This integration of computational methods into protein engineering not only accelerates the discovery of novel proteins but also enhances our understanding of protein functionality in biological systems.

3.2 Unsupervised Learning Techniques

Machine learning (ML) has emerged as a transformative tool in the field of protein function prediction, particularly through the application of unsupervised learning techniques. These methods enable the extraction of meaningful patterns and relationships from large datasets of protein sequences without the need for labeled data, thus facilitating predictions even for proteins with little or no sequence similarity to known functions.

One of the primary strategies involves the use of protein language models, which are trained on vast collections of unlabeled protein sequences. These models learn to capture the underlying evolutionary rules embedded in the sequences, allowing them to predict the functional effects of mutations. For instance, recent research has demonstrated that methods based on these language models can achieve superior results in predicting protein fitness from sequence data, thus providing insights into the effects of specific mutations on protein functionality [17].

Additionally, advanced machine learning approaches, such as deep learning, have been developed to enhance the prediction of protein functions from various sources of information, including sequence, structure, and interaction data. Deep learning models, particularly deep convolutional neural networks, can directly predict a variety of protein functions, such as Enzyme Commission (EC) numbers and Gene Ontology (GO) terms, from unaligned amino acid sequences. This direct approach contrasts with traditional methods that rely heavily on sequence alignment and comparison [3].

Moreover, semi-supervised learning techniques have been employed to address the challenges posed by the scarcity of labeled data in protein function prediction. For example, a study introduced a semi-supervised autoencoder-based approach where each autoencoder is trained to predict a specific biological process or molecular function. This method utilizes reconstruction losses from protein samples as features to classify sequences into their respective functions, achieving promising results [16].

Unsupervised learning techniques, including clustering and dimensionality reduction methods, are also utilized to analyze the relationships between protein sequences and their functions. These techniques can identify groups of proteins with similar characteristics or functions, thus providing insights into potential functional annotations for uncharacterized proteins [18].

Overall, the integration of unsupervised learning techniques with machine learning frameworks has significantly advanced the field of protein function prediction. These approaches not only enhance the accuracy of predictions but also facilitate the exploration of vast protein sequence spaces, ultimately leading to a better understanding of protein functionalities and their applications in biotechnology and medicine [14].

3.3 Deep Learning Applications

Machine learning, particularly deep learning, has significantly advanced the field of protein function prediction, leveraging vast amounts of available protein sequence data. This approach is crucial due to the challenges posed by traditional experimental methods, which are often time-consuming and costly. The integration of computational techniques allows for more efficient and scalable predictions of protein functions based on various data forms, including sequences, structures, and interactions.

Deep learning models have been designed to process and analyze protein sequences by learning intricate patterns and features. For instance, Dhanuka et al. (2022) proposed a semi-supervised autoencoder-based approach where 932 autoencoders were trained specifically for biological processes, and 585 for molecular functions. This method utilizes reconstruction losses from each autoencoder as features for classifying protein sequences into their corresponding functions, achieving promising results on test samples[16].

Furthermore, models like DeepGOZero have enhanced prediction capabilities by employing a model-theoretic approach to learn ontology embeddings, which enables zero-shot predictions for functions with limited or no training data. This model effectively uses formal axioms from the Gene Ontology (GO) to predict protein functions even in the absence of associated training examples[19].

Another innovative approach is the Prot2GO model, which integrates protein sequence data with protein-protein interaction (PPI) network data. By utilizing a convolutional neural network (CNN) to extract local features from sequences and a recurrent neural network (RNN) for capturing long-range dependencies, Prot2GO has achieved state-of-the-art performance in predicting protein functions[20].

Additionally, deep learning frameworks such as DeepFunc have shown that combining multiple derived feature information can significantly enhance prediction accuracy compared to methods relying on single feature types[21]. These models utilize various architectures, including deep convolutional networks and recurrent neural networks, to analyze the sequence data and make predictions about protein functions categorized under different GO terms.

Moreover, advancements in multi-modal approaches, such as MultiPredGO, leverage both protein sequence and structural information to improve prediction accuracy. This model incorporates a neuro-symbolic hierarchical classification structure, mirroring the dependencies inherent in GO classifications, which allows for a more nuanced prediction of protein functions[22].

In summary, machine learning, particularly through deep learning techniques, has transformed protein function prediction by enabling the analysis of complex biological data. These methods not only enhance predictive accuracy but also provide frameworks capable of interpreting relationships between protein structures and their functions, thus facilitating a deeper understanding of biological mechanisms and aiding in drug discovery and disease treatment[2][23][24].

4 Data Sources and Feature Extraction

4.1 Sequence Data

Machine learning (ML) has emerged as a powerful tool for predicting protein functions, particularly leveraging sequence data. The prediction of protein functions from sequences involves several critical steps, including data sourcing, feature extraction, and model training.

The primary data source for protein function prediction is the vast repository of protein sequences generated by high-throughput sequencing techniques. As a result of advancements in sequencing technology, there are now abundant protein sequences available, making them suitable candidates for computational analysis [14]. However, predicting protein functions based solely on sequence data presents unique challenges, especially for proteins with low or no sequence similarity to those of known functions [18].

To effectively utilize sequence data for function prediction, a variety of feature extraction techniques are employed. These techniques can be broadly categorized into several types:

1. Sequence-Based Features: These include the direct amino acid sequences of proteins. Methods such as one-hot encoding and k-mer frequency extraction are commonly used to convert sequences into numerical formats that can be fed into ML models. For instance, the use of Markov model classifiers and feature vectors based on sequence predictions is one approach to classify gene expression [25].

2. Physicochemical Properties: Features derived from the physicochemical characteristics of amino acids, such as hydrophobicity, charge, and molecular weight, are also integrated into the models. These properties provide additional context about how the protein might behave in biological systems [26].

3. Evolutionary Information: Tools like sequence alignment and phylogenetic profiling can yield features that reflect evolutionary relationships among proteins. This information can be particularly valuable for understanding functional similarities and differences among homologous proteins [13].

4. Deep Learning Approaches: Advanced ML techniques, particularly deep learning, have been increasingly utilized for feature extraction. For example, convolutional neural networks (CNNs) can transform protein sequences into two-dimensional images, allowing for the extraction of complex patterns that might be indicative of function [6]. Similarly, models like DeepGOPlus combine deep learning with sequence similarity to enhance prediction accuracy [7].

5. Multi-View and Multi-Scale Approaches: Recent advancements have led to the development of models that incorporate multi-view and multi-scale features, capturing a range of information from protein sequences. For example, the MMSMAPlus model utilizes various types of features, including overlapping property features and deep semantic features, to improve prediction performance [26].

In conclusion, machine learning leverages a combination of diverse data sources and sophisticated feature extraction techniques to predict protein functions from sequence data. The integration of evolutionary, physicochemical, and sequence-based features, along with advanced deep learning methodologies, enhances the accuracy and efficiency of protein function predictions. This approach not only facilitates the annotation of newly sequenced proteins but also aids in understanding the functional roles of proteins in biological systems [1][4].

4.2 Structural Data

Machine learning techniques have increasingly been applied to predict protein function by leveraging structural data, which provides critical insights into the spatial arrangements and interactions of proteins. A comprehensive understanding of protein function is essential for various biological and pharmacological applications, and structural data serves as a fundamental resource in this predictive endeavor.

One prominent approach involves the use of graph models that integrate sequential, structural, and chemical information. This method, as described by Borgwardt et al. (2005), utilizes graph kernels and support vector machine classification to predict functional class membership of proteins by creating a graph model from their sequence and structure. The model effectively captures relevant attributes, such as amino acid motifs and interaction partners, which are essential for inferring protein function[27].

Additionally, advancements in machine learning have led to the development of multi-modal models that incorporate both sequence and structural information. For instance, the multi-modal protein function prediction model (MMPFP) integrates protein sequence and structure data through graph convolutional networks (GCN), convolutional neural networks (CNN), and Transformer models. This integration enhances the predictive accuracy for molecular function, biological processes, and cellular components by providing richer spatial and functional insights that are often overlooked when relying solely on sequence data[11].

Feature extraction is another critical aspect of utilizing structural data for machine learning-based predictions. For example, segmented distribution and segmented auto-covariance feature extraction methods have been proposed to capture both local and global discriminatory information from evolutionary profiles and predicted secondary structures. This approach significantly enhances the accuracy of structural class predictions, achieving over 90% accuracy on benchmark datasets[28]. Furthermore, Mirzaei et al. (2019) introduced scoring functions based on machine learning to assess the quality of predicted protein structures, highlighting the importance of selecting the best decoys through effective feature integration[29].

The holographic convolutional neural network (H-CNN) represents another innovative approach, focusing on modeling amino acid preferences in protein structures. This method accurately predicts the impact of mutations on protein stability and binding, thus facilitating a deeper understanding of the structure-function relationship in proteins[5].

In summary, machine learning predicts protein function by employing structural data through various methodologies, including graph models, multi-modal approaches, and advanced feature extraction techniques. These strategies enhance the accuracy of predictions by capturing essential spatial and functional information that is pivotal for understanding protein roles in biological systems.

4.3 Interaction Networks

Machine learning has emerged as a powerful tool for predicting protein function, leveraging various data sources and advanced feature extraction techniques, particularly through the analysis of interaction networks. The fundamental principle behind these predictions is the utilization of large-scale biological data to infer the functions of proteins based on their interactions with other biomolecules.

One of the prominent approaches involves the analysis of protein-protein interaction (PPI) networks. These networks consist of proteins as nodes and their interactions as edges, which provide critical insights into the functional relationships among proteins. For instance, Saha et al. (2014) proposed methods that utilize neighborhood properties within these interaction networks to predict protein functions. Their approach, FunPred, incorporates scoring techniques based on neighborhood ratios and functional similarities, achieving an accuracy of around 87% across various functional groups in yeast proteins[30].

Moreover, machine learning techniques can integrate multiple types of data, including sequence, structural, and interaction data. Wang et al. (2023) introduced MMSMAPlus, a multi-view model that captures diverse features from protein sequences and integrates them with interaction network data to enhance prediction accuracy[26]. This model employs a multi-view adaptive decision mechanism, which allows for comprehensive decision-making based on various views of data, thereby improving the robustness of predictions.

In another significant development, Zhang et al. (2023) presented the Prot2GO model, which combines protein sequence data with PPI network data to predict protein functions effectively. This model utilizes convolutional neural networks (CNNs) for sequence data and applies an improved random walk algorithm for feature extraction from the PPI network[20]. By integrating these two data sources, Prot2GO can capture both local features of protein sequences and the broader context provided by interaction networks, leading to superior performance in function prediction.

Additionally, machine learning methods often rely on feature importance analysis to understand which features contribute most significantly to predictions. Rodríguez-Pérez and Bajorath (2021) demonstrated that feature importance correlation analysis could reveal functional relationships between proteins based on their binding characteristics to compounds. This approach underscores the ability of machine learning to uncover hidden relationships in biological data, further enhancing the understanding of protein functions[31].

Overall, the integration of interaction networks with advanced machine learning techniques not only improves the accuracy of protein function predictions but also provides a deeper understanding of the biological roles of proteins. As the field continues to evolve, the development of more sophisticated models that can harness the complexity of biological data will be crucial for advancing our knowledge in functional genomics and drug discovery.

5 Challenges and Limitations

5.1 Data Quality and Availability

Machine learning (ML) has emerged as a significant tool in predicting protein functions, leveraging various data types such as protein sequences, structures, and interaction networks. However, the effectiveness of these methods is heavily influenced by data quality and availability, which presents several challenges.

One major issue is the incomplete knowledge inherent in current biological databases. The success of supervised machine learning in protein function prediction relies on having a gold standard of protein functions, which is often lacking due to incomplete gene annotations. This incompleteness can adversely affect the evaluation of machine learning models, leading to misleading performance assessments. For instance, studies have shown that while ML approaches can generalize and predict novel biology even from incomplete data, the evaluation of their performance is complicated when the gold standard is sparse. In such scenarios, different methods may be differentially underestimated, making comparative evaluations problematic (Huttenhower et al. 2009) [32].

Moreover, the reliance on existing datasets can introduce biases, as the performance of ML models typically degrades when test proteins deviate from the training data distribution. This phenomenon emphasizes the need for diverse and comprehensive datasets to ensure that models can generalize well across different protein types. A systematic review highlights that while traditional experimental techniques are insufficient for the rapidly growing annotation needs, computational methods, particularly those based on ML, have flourished. However, the challenge remains to effectively utilize available data while addressing issues related to data quality and completeness (Yan et al. 2023) [4].

Furthermore, the integration of machine learning with biophysics-based knowledge has been proposed as a potential solution to enhance prediction accuracy. By combining these approaches, researchers aim to improve the robustness of protein property predictions, particularly in cases where training data is limited (Nisonoff et al. 2023) [33]. This hybrid approach allows for a more balanced reliance on empirical data and theoretical models, which can be crucial when working with sparse datasets.

In summary, while machine learning presents promising avenues for predicting protein functions, challenges related to data quality and availability remain significant hurdles. Addressing these challenges through improved data collection, validation methods, and the integration of diverse information sources will be critical for advancing the field of protein function prediction.

5.2 Model Interpretability

Machine learning has become a prominent approach for predicting protein function, leveraging various types of data such as protein sequences, structures, and interaction networks. However, the application of machine learning in this domain faces several challenges and limitations, particularly regarding model interpretability.

One of the primary challenges in predicting protein function using machine learning is the reliance on large datasets. Machine learning models require substantial amounts of data to train effectively. For proteins that have low or no sequence similarity to known functional proteins, predicting their function becomes particularly difficult. Recent advances have utilized machine learning methods that predict functional classes independent of sequence similarity, demonstrating promising potential for low- and non-homologous proteins [18]. Nonetheless, the performance of these methods can vary significantly based on the datasets used and the specific parameters chosen [18].

In terms of model interpretability, the complexity of machine learning algorithms can hinder the understanding of how predictions are made. Traditional experimental techniques often provided clear insights into protein functions, but machine learning models, especially deep learning approaches, can act as "black boxes," making it difficult to decipher the rationale behind their predictions [1]. This lack of transparency can lead to challenges in validating predictions and gaining trust from biologists who may be less familiar with computational methodologies [34].

Furthermore, the interpretability of models is crucial for advancing biological understanding and generating new hypotheses. Models that are comprehensible can increase the confidence of researchers in the predictions made, leading to novel insights and potential error detection in data [34]. Thus, while machine learning holds significant promise for protein function prediction, the development of interpretable models remains a critical area for future research.

Recent studies have suggested various strategies to improve interpretability, such as using simpler models or integrating biological knowledge into machine learning frameworks [33]. Additionally, employing techniques that provide insights into feature importance can aid in understanding the factors that contribute to predictions [33].

In summary, while machine learning offers powerful tools for predicting protein function, challenges such as data dependency and model interpretability must be addressed to enhance the reliability and acceptance of these predictions within the biological community. The ongoing evolution of methodologies and a focus on creating comprehensible models will be essential for advancing this field [1][4].

5.3 Generalization to Novel Proteins

Machine learning has emerged as a powerful tool for predicting protein function from amino acid sequences, particularly in the context of proteins with unknown functions. The process involves training classifiers on proteins with known functions, allowing these models to identify patterns and make predictions about the functions of novel proteins. However, this approach is not without its challenges and limitations, particularly concerning generalization to novel proteins.

One significant challenge in predicting protein function using machine learning is the inherent differences between proteins with known and unknown functions. Research has shown that proteins from different bacterial species exhibit considerable variability, which can complicate the transferability of learned classifiers across species boundaries. Despite these differences, functional classifiers have been found to generalize successfully across species, suggesting that some predictive power can be maintained even when applied to novel proteins [35].

Moreover, machine learning methods are particularly valuable for predicting the functional class of proteins that exhibit low or no sequence similarity to proteins of known function. This capability is crucial because many newly discovered proteins do not share obvious homologs, making traditional sequence alignment methods ineffective. Machine learning approaches that rely on sequence-derived properties, independent of sequence similarity, have shown promising results in addressing this issue [18].

Nonetheless, the generalization of machine learning models to novel proteins remains a concern. The performance of these models can be influenced by factors such as the training dataset's size and diversity, as well as the choice of parameters during model development. There is a risk of overfitting, where a model performs well on the training data but fails to accurately predict functions for unseen proteins [36]. Therefore, careful consideration must be given to the design of experiments and the validation of models to ensure that predictions are reliable and applicable to novel protein contexts.

In conclusion, while machine learning holds significant promise for predicting protein function, challenges related to generalization to novel proteins must be addressed. Continued research into the development of robust classifiers, as well as the integration of diverse datasets and improved methodologies, will be essential for enhancing the accuracy and applicability of these predictive models in the realm of protein bioinformatics [4][37].

6 Future Directions and Applications

6.1 Integration of Multi-Omics Data

Machine learning has significantly advanced the prediction of protein functions, particularly through the integration of multi-omics data. This integration involves utilizing diverse biological data sources, including genomic, proteomic, and interaction network data, to enhance the accuracy and comprehensiveness of protein function predictions.

One of the pivotal approaches in this domain is the use of ensemble learning methods, which combine multiple predictive models to improve performance. For instance, Guoxian Yu et al. (2016) developed a method called SimNet, which semantically integrates multiple functional association networks derived from heterogeneous data sources, using Gene Ontology (GO) annotations to capture semantic similarities between proteins. This method constructs a composite network and applies a network-based classifier to predict protein functions, demonstrating superior performance compared to single-source methods[38].

Additionally, the integration of various data modalities is crucial. Xiaoshuai Zhang et al. (2023) introduced the Prot2GO model, which combines protein sequence data with protein-protein interaction (PPI) network data. This model employs deep learning techniques, such as convolutional and recurrent neural networks, to extract features from both sequence and interaction data, ultimately leading to enhanced predictions of protein functions[20].

Another notable example is the MultiPredGO approach, which integrates protein sequence, secondary structure, and interaction information. This method utilizes convolutional neural networks to extract features from different data modalities and employs a neuro-symbolic hierarchical classification model that reflects the structure of GO, thereby effectively predicting dependent protein functions[22].

Furthermore, the use of multi-view and multi-scale models, as seen in the MMSMAPlus framework, emphasizes the extraction of multi-view features from protein sequences, capturing both local patterns and long-range dependencies. This comprehensive feature extraction process allows for a more nuanced understanding of protein functions[26].

Machine learning techniques also facilitate the identification of relevant data sources for protein function prediction. Seokha Ko and Hyunju Lee (2009) highlighted the importance of systematic feature selection methods to assess the contribution of various genomic data sets, ensuring that only the most relevant sources are integrated into predictive models[39].

In summary, the integration of multi-omics data through advanced machine learning techniques not only enhances the predictive accuracy of protein functions but also addresses the challenges posed by the complexity of biological systems. The continued development of these integrative approaches holds great promise for future applications in functional genomics, drug discovery, and personalized medicine.

6.2 Enhancements in Model Development

Machine learning (ML) has significantly transformed the field of protein function prediction, enabling researchers to uncover complex relationships between protein sequences, structures, and their respective functions. The application of ML methods facilitates a more data-driven approach to predicting protein functions, overcoming the limitations of traditional techniques.

The integration of ML into protein function prediction encompasses several methodologies, including supervised learning models that derive sequence-function mappings from experimental data. These models allow for the identification of protein properties without necessitating a detailed understanding of the underlying biological mechanisms. For instance, machine-learning-guided directed evolution has been shown to optimize protein functions effectively by learning from characterized variants and selecting sequences that are likely to exhibit improved properties (Yang et al. 2019) [13].

Recent advancements have highlighted the importance of deep learning techniques, which have demonstrated superior performance over conventional ML methods. These deep learning models utilize large datasets generated from high-throughput sequencing and experimental assays, enabling them to learn intricate patterns in protein data. For example, deep learning frameworks like DeepFunc have been reported to enhance prediction accuracy by integrating multiple derived feature information from protein sequences and interaction networks (Lv et al. 2019) [21].

Future directions in ML-based protein function prediction are poised to focus on several key enhancements in model development. Firstly, there is a growing trend towards utilizing protein language models that leverage vast amounts of protein sequence data to extract semantic information relevant to function prediction. These models can significantly improve prediction accuracy by capturing the nuances of protein sequences (Chen et al. 2025) [40].

Moreover, the development of generative models that can learn the underlying distribution of protein functions in sequence space presents a promising avenue for future research. These models could facilitate the discovery of novel protein functions and aid in the exploration of unseen sequence space (Freschlin et al. 2022) [14].

Another promising area of development involves the incorporation of multi-information sources, such as structural data and protein-protein interaction networks, into predictive models. This holistic approach allows for a more comprehensive understanding of protein functions by accounting for the interdependencies between structure, dynamics, and stability (Avery et al. 2022) [8].

In conclusion, the evolution of machine learning methodologies continues to drive significant advancements in the prediction of protein functions. As these techniques become more sophisticated and integrated with diverse data sources, they hold the potential to not only enhance the accuracy of predictions but also to uncover new biological insights that were previously inaccessible.

6.3 Implications for Drug Discovery and Personalized Medicine

Machine learning (ML) has emerged as a powerful tool in predicting protein function, significantly influencing drug discovery and personalized medicine. The process of predicting protein function using ML involves various methodologies that leverage computational approaches to analyze protein sequences, structures, and interactions. These methods are increasingly important as traditional experimental techniques become inadequate for the rapid growth of protein sequence annotation.

One of the primary approaches in ML for protein function prediction is based on analyzing protein sequences and structures. For instance, recent advancements in deep learning methods have shown promise in improving the accuracy of protein function predictions. These models utilize large datasets to learn complex relationships between protein sequences and their functions, thereby generating hypotheses for biological experiments and facilitating the understanding of biological systems (Boadu et al., 2025) [1].

In addition to sequence-based methods, ML techniques also incorporate information from protein-protein interaction (PPI) networks and other biological data. For example, a systematic review highlighted various strategies for predicting protein functions, emphasizing the fusion of multi-information sources, including sequence, structure, and interaction data, to enhance prediction accuracy (Yan et al., 2023) [4]. Furthermore, machine learning can identify novel drug targets by analyzing features derived from protein sequences and network properties, leading to the development of predictive models that score proteins based on their potential as drug targets (Dezső & Ceccarelli, 2020) [41].

The implications of these advancements in ML for drug discovery are profound. Accurate predictions of protein functions allow for the rational design of small molecules that can selectively interact with their targets, thereby modulating their functions effectively. For instance, the advent of AlphaFold2 has revolutionized the field by providing highly accurate predictions of protein structures, which are crucial for drug design (Schauperl & Denny, 2022) [42]. This capability enables researchers to design compounds that are more likely to succeed in clinical trials by ensuring that they interact with the correct protein targets.

In the context of personalized medicine, the ability to predict protein functions and interactions can lead to tailored therapeutic strategies that consider individual patient profiles. By integrating patient-specific data with ML algorithms, it is possible to predict how different patients will respond to specific treatments based on their unique protein expressions and functions (Su et al., 2024) [43]. This personalized approach not only enhances treatment efficacy but also minimizes adverse effects, aligning with the core principles of personalized medicine.

Moreover, future directions in ML applications for protein function prediction include improving the integration of diverse biological data types, enhancing model interpretability, and addressing existing challenges such as data scarcity and the need for robust validation methods (Avery et al., 2022) [8]. The continuous evolution of ML techniques promises to further accelerate drug discovery processes, enabling the identification of new drug targets and facilitating the development of innovative therapeutic strategies tailored to individual patient needs.

In summary, machine learning plays a pivotal role in predicting protein function, with significant implications for drug discovery and personalized medicine. By leveraging advanced computational methods, researchers can enhance the understanding of protein roles in biological systems, streamline the drug development process, and create more effective, individualized treatment plans.

7 Conclusion

This review highlights the transformative impact of machine learning (ML) on the prediction of protein functions, underscoring its ability to handle the complexities of biological data more efficiently than traditional methods. Key findings indicate that ML approaches, particularly deep learning techniques, significantly enhance predictive accuracy by leveraging diverse data sources, including sequence, structural, and interaction data. Despite the promising advancements, challenges remain, particularly in data quality, model interpretability, and the generalization of predictions to novel proteins. Future research should focus on integrating multi-omics data, developing robust and interpretable models, and addressing existing limitations to further enhance the reliability and applicability of protein function predictions. The potential applications of these advancements in drug discovery and personalized medicine highlight the importance of continued exploration in this rapidly evolving field, paving the way for innovative therapeutic strategies and a deeper understanding of biological systems.

References

• [1] Frimpong Boadu;Ahhyun Lee;Jianlin Cheng. Deep learning methods for protein function prediction.. Proteomics(IF=3.9). 2025. PMID:38996351. DOI: 10.1002/pmic.202300471.
• [2] Richa Dhanuka;Jyoti Prakash Singh;Anushree Tripathi. A Comprehensive Survey of Deep Learning Techniques in Protein Function Prediction.. IEEE/ACM transactions on computational biology and bioinformatics(IF=3.4). 2023. PMID:37027658. DOI: 10.1109/TCBB.2023.3247634.
• [3] Theo Sanderson;Maxwell L Bileschi;David Belanger;Lucy J Colwell. ProteInfer, deep neural networks for protein functional inference.. eLife(IF=6.4). 2023. PMID:36847334. DOI: .
• [4] Tian-Ci Yan;Zi-Xuan Yue;Hong-Quan Xu;Yu-Hong Liu;Yan-Feng Hong;Gong-Xing Chen;Lin Tao;Tian Xie. A systematic review of state-of-the-art strategies for machine learning-based protein function prediction.. Computers in biology and medicine(IF=6.3). 2023. PMID:36680931. DOI: 10.1016/j.compbiomed.2022.106446.
• [5] Michael N Pun;Andrew Ivanov;Quinn Bellamy;Zachary Montague;Colin LaMont;Philip Bradley;Jakub Otwinowski;Armita Nourmohammad. Learning the shape of protein microenvironments with a holographic convolutional neural network.. Proceedings of the National Academy of Sciences of the United States of America(IF=9.1). 2024. PMID:38300863. DOI: 10.1073/pnas.2300838121.
• [6] Samia Tasnim Sara;Md Mehedi Hasan;Ahsan Ahmad;Swakkhar Shatabda. Convolutional neural networks with image representation of amino acid sequences for protein function prediction.. Computational biology and chemistry(IF=3.1). 2021. PMID:33930742. DOI: 10.1016/j.compbiolchem.2021.107494.
• [7] Maxat Kulmanov;Robert Hoehndorf. DeepGOPlus: improved protein function prediction from sequence.. Bioinformatics (Oxford, England)(IF=5.4). 2020. PMID:31350877. DOI: 10.1093/bioinformatics/btz595.
• [8] Chris Avery;John Patterson;Tyler Grear;Theodore Frater;Donald J Jacobs. Protein Function Analysis through Machine Learning.. Biomolecules(IF=4.8). 2022. PMID:36139085. DOI: 10.3390/biom12091246.
• [9] Orhun Vural;Leon Jololian. Machine learning approaches for predicting protein-ligand binding sites from sequence data.. Frontiers in bioinformatics(IF=3.9). 2025. PMID:39963299. DOI: 10.3389/fbinf.2025.1520382.
• [10] Richa Dhanuka;Jyoti Prakash Singh. Protein function prediction using functional inter-relationship.. Computational biology and chemistry(IF=3.1). 2021. PMID:34736126. DOI: 10.1016/j.compbiolchem.2021.107593.
• [11] Yu Mao;WenHui Xu;Yue Shun;LongXin Chai;Lei Xue;Yong Yang;Mei Li. A multimodal model for protein function prediction.. Scientific reports(IF=3.9). 2025. PMID:40140535. DOI: 10.1038/s41598-025-94612-y.
• [12] Moritz Ertelt;Rocco Moretti;Jens Meiler;Clara T Schoeder. Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.. Science advances(IF=12.5). 2025. PMID:39937901. DOI: 10.1126/sciadv.adr7338.
• [13] Kevin K Yang;Zachary Wu;Frances H Arnold. Machine-learning-guided directed evolution for protein engineering.. Nature methods(IF=32.1). 2019. PMID:31308553. DOI: 10.1038/s41592-019-0496-6.
• [14] Chase R Freschlin;Sarah A Fahlberg;Philip A Romero. Machine learning to navigate fitness landscapes for protein engineering.. Current opinion in biotechnology(IF=7.0). 2022. PMID:35413604. DOI: 10.1016/j.copbio.2022.102713.
• [15] Sam Gelman;Sarah A Fahlberg;Pete Heinzelman;Philip A Romero;Anthony Gitter. Neural networks to learn protein sequence-function relationships from deep mutational scanning data.. Proceedings of the National Academy of Sciences of the United States of America(IF=9.1). 2021. PMID:34815338. DOI: 10.1073/pnas.2104878118.
• [16] Richa Dhanuka;Anushree Tripathi;Jyoti P Singh. A Semi-Supervised Autoencoder-Based Approach for Protein Function Prediction.. IEEE journal of biomedical and health informatics(IF=6.8). 2022. PMID:35349463. DOI: 10.1109/JBHI.2022.3163150.
• [17] Yang Qu;Zitong Niu;Qiaojiao Ding;Taowa Zhao;Tong Kong;Bing Bai;Jianwei Ma;Yitian Zhao;Jianping Zheng. Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.. International journal of molecular sciences(IF=4.9). 2023. PMID:38003686. DOI: 10.3390/ijms242216496.
• [18] Lianyi Han;Juan Cui;Honghuang Lin;Zhiliang Ji;Zhiwei Cao;Yixue Li;Yuzong Chen. Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity.. Proteomics(IF=3.9). 2006. PMID:16791826. DOI: 10.1002/pmic.200500938.
• [19] Maxat Kulmanov;Robert Hoehndorf. DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms.. Bioinformatics (Oxford, England)(IF=5.4). 2022. PMID:35758802. DOI: 10.1093/bioinformatics/btac256.
• [20] Xiaoshuai Zhang;Lixin Wang;Hucheng Liu;Xiaofeng Zhang;Bo Liu;Yadong Wang;Junyi Li. Prot2GO: Predicting GO Annotations From Protein Sequences and Interactions.. IEEE/ACM transactions on computational biology and bioinformatics(IF=3.4). 2023. PMID:34971539. DOI: 10.1109/TCBB.2021.3139841.
• [21] Zhibin Lv;Chunyan Ao;Quan Zou. Protein Function Prediction: From Traditional Classifier to Deep Learning.. Proteomics(IF=3.9). 2019. PMID:31187588. DOI: 10.1002/pmic.201900119.
• [22] Swagarika Jaharlal Giri;Pratik Dutta;Parth Halani;Sriparna Saha. MultiPredGO: Deep Multi-Modal Protein Function Prediction by Amalgamating Protein Structure, Sequence, and Interaction Information.. IEEE journal of biomedical and health informatics(IF=6.8). 2021. PMID:32897865. DOI: 10.1109/JBHI.2020.3022806.
• [23] Wenkang Wang;Yunyan Shuai;Min Zeng;Wei Fan;Min Li. DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.. Nature communications(IF=15.7). 2025. PMID:39746897. DOI: 10.1038/s41467-024-54816-8.
• [24] Jiaqing Xie;Yuqiang Li;Tianfan Fu. DeepProtein: deep learning library and benchmark for protein sequence learning.. Bioinformatics (Oxford, England)(IF=5.4). 2025. PMID:40388205. DOI: 10.1093/bioinformatics/btaf165.
• [25] Kyoung Tak Cho;Taner Z Sen;Carson M Andorf. Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.. Frontiers in artificial intelligence(IF=4.7). 2022. PMID:35719692. DOI: 10.3389/frai.2022.830170.
• [26] Zhongyu Wang;Zhaohong Deng;Wei Zhang;Qiongdan Lou;Kup-Sze Choi;Zhisheng Wei;Lei Wang;Jing Wu. MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.. Briefings in bioinformatics(IF=7.7). 2023. PMID:37258453. DOI: 10.1093/bib/bbad201.
• [27] Karsten M Borgwardt;Cheng Soon Ong;Stefan Schönauer;S V N Vishwanathan;Alex J Smola;Hans-Peter Kriegel. Protein function prediction via graph kernels.. Bioinformatics (Oxford, England)(IF=5.4). 2005. PMID:15961493. DOI: 10.1093/bioinformatics/bti1007.
• [28] Abdollah Dehzangi;Kuldip Paliwal;James Lyons;Alok Sharma;Abdul Sattar. Proposing a highly accurate protein structural class predictor using segmentation-based features.. BMC genomics(IF=3.7). 2014. PMID:24564476. DOI: 10.1186/1471-2164-15-S1-S2.
• [29] Shokoufeh Mirzaei;Tomer Sidi;Chen Keasar;Silvia Crivelli. Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning.. IEEE/ACM transactions on computational biology and bioinformatics(IF=3.4). 2019. PMID:28113636. DOI: 10.1109/TCBB.2016.2602269.
• [30] Sovan Saha;Piyali Chatterjee;Subhadip Basu;Mahantapas Kundu;Mita Nasipuri. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis.. Cellular & molecular biology letters(IF=10.2). 2014. PMID:25424913. DOI: 10.2478/s11658-014-0221-5.
• [31] Raquel Rodríguez-Pérez;Jürgen Bajorath. Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics.. Scientific reports(IF=3.9). 2021. PMID:34244588. DOI: 10.1038/s41598-021-93771-y.
• [32] Curtis Huttenhower;Matthew A Hibbs;Chad L Myers;Amy A Caudy;David C Hess;Olga G Troyanskaya. The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction.. Bioinformatics (Oxford, England)(IF=5.4). 2009. PMID:19561015. DOI: 10.1093/bioinformatics/btp397.
• [33] Hunter Nisonoff;Yixin Wang;Jennifer Listgarten. Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction.. ACS synthetic biology(IF=3.9). 2023. PMID:37888887. DOI: 10.1021/acssynbio.3c00217.
• [34] Alex A Freitas;Daniela C Wieser;Rolf Apweiler. On the importance of comprehensible classification models for protein function prediction.. IEEE/ACM transactions on computational biology and bioinformatics(IF=3.4). 2010. PMID:20150679. DOI: 10.1109/TCBB.2008.47.
• [35] Ali Al-Shahib;Rainer Breitling;David R Gilbert. Predicting protein function by machine learning on amino acid sequences--a critical evaluation.. BMC genomics(IF=3.7). 2007. PMID:17374164. DOI: 10.1186/1471-2164-8-78.
• [36] Ian Walsh;Gianluca Pollastri;Silvio C E Tosatto. Correct machine learning on protein sequences: a peer-reviewing perspective.. Briefings in bioinformatics(IF=7.7). 2016. PMID:26411473. DOI: 10.1093/bib/bbv082.
• [37] Constance J Jeffery. Current successes and remaining challenges in protein function prediction.. Frontiers in bioinformatics(IF=3.9). 2023. PMID:37576715. DOI: 10.3389/fbinf.2023.1222182.
• [38] Guoxian Yu;Guangyuan Fu;Jun Wang;Hailong Zhu. Predicting Protein Function via Semantic Integration of Multiple Networks.. IEEE/ACM transactions on computational biology and bioinformatics(IF=3.4). 2016. PMID:26800544. DOI: 10.1109/TCBB.2015.2459713.
• [39] Seokha Ko;Hyunju Lee. Integrative approaches to the prediction of protein functions based on the feature selection.. BMC bioinformatics(IF=3.3). 2009. PMID:20043848. DOI: 10.1186/1471-2105-10-455.
• [40] Jia-Ying Chen;Jing-Fu Wang;Yue Hu;Xin-Hui Li;Yu-Rong Qian;Chao-Lin Song. Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.. Frontiers in bioengineering and biotechnology(IF=4.8). 2025. PMID:39906415. DOI: 10.3389/fbioe.2025.1506508.
• [41] Zoltán Dezső;Michele Ceccarelli. Machine learning prediction of oncology drug targets based on protein and network properties.. BMC bioinformatics(IF=3.3). 2020. PMID:32171238. DOI: 10.1186/s12859-020-3442-9.
• [42] Michael Schauperl;Rajiah Aldrin Denny. AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges.. Journal of chemical information and modeling(IF=5.3). 2022. PMID:35727311. DOI: 10.1021/acs.jcim.2c00026.
• [43] Junwen Su;Lamei Yang;Ziran Sun;Xianquan Zhan. Personalized Drug Therapy: Innovative Concept Guided With Proteoformics.. Molecular & cellular proteomics : MCP(IF=5.5). 2024. PMID:38354979. DOI: 10.1016/j.mcpro.2024.100737.

MaltSci Intelligent Research Services

Create Your Professional Reports Easily

Search for more papers on MaltSci.com

Machine Learning · Protein Function Prediction · Deep Learning · Bioinformatics · Multi-Omics Data

---

© 2025 MaltSci