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Results for “protein representation learning”

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PubMed2005

A docking analysis of the statistical physics of protein-protein recognition.

We describe protein-protein recognition within the frame of the random energy model of statistical physics. We simulate, by docking the component proteins, the process of association of two proteins that form a complex. We obtain the energy spectrum of a set of protein-protein complexes of known three-dimensional structure by performing docking in random orientations and scoring the models thus generated. We use a coarse protein representation where each amino acid residue is replaced by its Voronoï cell, and derive a scoring function by applying the evolutionary learning program ROGER to a set of parameters measured on that representation. Taking the scores of the docking models to be interaction energies, we obtain energy spectra for the complexes and fit them to a Gaussian distribution, from which we derive physical parameters such as a glass transition temperature and a specificity transition temperature.

Biophysics↗
PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗
PubMed2001

PSD95 protein level rises in murine somatosensory cortex after sensory training.

Proteins of the postsynaptic density are implicated in mechanisms of synaptic plasticity. We examined involvement of PSD95 and alphaCaMkII in learning-dependent plastic changes of representational maps in somatosensory cortex of mice. The barrel cortex of mice was examined following a 3 day long classical conditioning training, in which activation of facial vibrissae was linked to an aversive stimulus. This procedure produced expansion of cortical representations of vibrissae involved in the training. In subcellular fraction enriched in postsynaptic densities from the barrel cortex, it was estimated by Western blotting that the level of PSD95 increased after the training by about 50%, while the level of CaMkII remained unchanged. The results indicate involvement of PSD95 in learning-dependent cortical plasticity.

Afferent Pathways↗
PubMed1993

Representation for discovery of protein motifs.

There are several dimensions and levels of complexity in which information on protein motifs may be available. For example, one-dimensional sequence motifs may be associated with secondary structure identifiers. Alternatively, three-dimensional information on polypeptide segments may be used to induce prototypical three-dimensional structure templates. This paper surveys various representations encountered in the protein motif discovery literature. Many of the representations are based on incompatible semantics, making difficult the comparison and combination of previous results. To make better use of machine learning techniques and to provide for an integrated knowledge representation framework, a general representation language--in which all types of motifs can be encoded and given a uniform semantics--is required. In this paper we propose such a model, called a spatial description logic, and present a machine learning approach based on the model.

Amino Acids↗
PubMed2026

MegaPlantTF: a machine learning framework for comprehensive identification and classification of plant transcription factors.

MOTIVATION: Understanding the role of transcription factors (TFs) in plants is essential for the study of gene regulation and various biological processes. However, both TF detection and classification remain challenging due to the great diversity and complexity of these proteins. Conventional approaches, such as BLAST, often suffer from high computational complexity and limited performance on less common TF families. RESULTS: We introduce MegaPlantTF, the first comprehensive machine learning and deep learning framework for the prediction (TF versus non-TF) and classification (family-level) of plant TFs. Our method employs k-mer-based protein representations and a two-stage architecture combining a deep feed-forward neural network with a stacking ensemble classifier. To ensure robust performance assessment, we report micro-, macro-, and weighted-average performance metrics, providing a holistic evaluation of both frequent and underrepresented TF families. Additionally, we employ threshold-based evaluation to calibrate confidence in TF detection. The results show that MegaPlantTF achieves strong accuracy and precision, particularly with a k-mer size of 3 and a classification threshold of 0.5, and maintains stable performance even under stringent thresholds. In addition to the standard cross-validation tests, a use case study on Sorghum bicolor confirms that our method performs strongly in the genome-wide analysis, making it highly suitable for large-scale TF identification and classification tasks. MegaPlantTF represents a novel contribution by integrating k-mer encoding, binary family-specific classifiers, and a two-stage stacking ensemble into a unified, reproducible framework for large-scale plant TF identification and classification. AVAILABILITY AND IMPLEMENTATION: MegaPlantTF is freely accessible through a public web server available at https://bioinformatics.um6p.ma/MegaPlantTF. The complete source code, including pretrained models and example datasets, is available at https://github.com/Bioinformatics-UM6P/MegaPlantTF.

Transcription Factors↗
PubMed2026

Deep generative models in biological sequence and structure analysis and design.

Deep generative models have transformed biological sequence modeling from predictive analysis toward increasingly controllable design. Early biological applications of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) established latent representation learning and sequence synthesis, while recent advances in transformer-based language models, discrete diffusion, flow-matching, and multimodal generative frameworks have substantially expanded the scope of biological design. This review examines generative models for DNA, RNA, and protein sequence design, emphasizing how different model classes represent biological constraints, operate over discrete and continuous spaces, and integrate sequence, structure, and function. We compare VAEs, GANs, autoregressive and masked language models, diffusion models, and flow-based approaches across genomics, transcriptomics, and proteomics, with particular attention to controllability, long-range dependency modeling, structural grounding, generalization, and experimental utility. We further examine evaluation strategies, out-of-distribution generalization, and closed-loop design-build-test-learn workflows that connect in silico generation with empirical validation. We distinguish fundamental modality-dependent constraints including sequence discreteness, context length, structural coupling, and physical or thermodynamic requirements from architecture-dependent advantages that reflect the current state of the field. Current studies suggest that long-context models are particularly useful for genome-scale representation and sequence modeling, whereas structure-aware diffusion, flow-based, and inverse-folding approaches provide better frameworks for geometry-constrained RNA and protein design. This perspective provides a critical framework for understanding the present capabilities, limitations, and convergence of generative approaches toward reliable and experimentally grounded biological design.

Biological sequence analysis↗
PubMed1994

A shape-based machine learning tool for drug design.

Building predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.

Algorithms↗
PubMed2002

Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners.

MOTIVATION: Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction. RESULTS: We develop a new set of flexible machine learning architectures for the prediction of contact maps, as well as other information processing and pattern recognition tasks. The architectures can be viewed as recurrent neural network implemantations of a class of Bayesian networks we call generalized input-output HMMs (GIOHMMs). For the specific case of contact maps, contextual information is propagated laterally through four hidden planes, one for each cardinal corner. We show that these architectures can be trained from examples and yield contact map predictors that outperform previously reported methods. While several extensions and improvements are in progress, the current version can accurately predict 60.5% of contacts at a distance cutoff of 8 A and 45% of distant contacts at 10 A, for proteins of length up to 300.

Algorithms↗
PubMed2007

Prediction-based fingerprints of protein-protein interactions.

The recognition of protein interaction sites is an important intermediate step toward identification of functionally relevant residues and understanding protein function, facilitating experimental efforts in that regard. Toward that goal, the authors propose a novel representation for the recognition of protein-protein interaction sites that integrates enhanced relative solvent accessibility (RSA) predictions with high resolution structural data. An observation that RSA predictions are biased toward the level of surface exposure consistent with protein complexes led the authors to investigate the difference between the predicted and actual (i.e., observed in an unbound structure) RSA of an amino acid residue as a fingerprint of interaction sites. The authors demonstrate that RSA prediction-based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure-derived and other features considered before. On the basis of these observations, the authors developed a new method for the prediction of protein-protein interaction sites, using machine learning approaches to combine the most informative features into the final predictor. For training and validation, the authors used several large sets of protein complexes and derived from them nonredundant representative chains, with interaction sites mapped from multiple complexes. Alternative machine learning techniques are used, including Support Vector Machines and Neural Networks, so as to evaluate the relative effects of the choice of a representation and a specific learning algorithm. The effects of induced fit and uncertainty of the negative (noninteracting) class assignment are also evaluated. Several representative methods from the literature are reimplemented to enable direct comparison of the results. Using rigorous validation protocols, the authors estimated that the new method yields the overall classification accuracy of about 74% and Matthews correlation coefficients of 0.42, as opposed to up to 70% classification accuracy and up to 0.3 Matthews correlation coefficient for methods that do not utilize RSA prediction-based fingerprints. The new method is available at http://sppider.cchmc.org.

Artificial Intelligence↗
PubMed2026

Fine-grained structural classification of biosynthetic gene cluster-encoded products.

MOTIVATION: Biosynthetic gene clusters (BGCs) are responsible the biosynthesis of many natural products, including a multitude of effective therapeutics and their precursors. Advances in genomic data collection as well as computational techniques have made it possible to identify BGCs at scale. However, accurately determining the types of BGC-encoded products from genomic content remains elusive. RESULTS: Here, we introduce BGC annotation tool (BGCat), a machine learning method for fine-grained structural classification of BGC-encoded products, leveraging the NPClassifier natural product nomenclature. Our method leverages a pre-trained protein language model for creating meaningful gene representations and a deep neural network for class label prediction. We show the method outperforms state-of-the-art approaches in coarse-grained product classification and is effective for detailed classification. We implement a clustering-based augmentation strategy for BGC-product relationships, addressing a crucial gap in the available datasets. We then introduce the concept of product class profiles of gene cluster families (GCFs), associating each GCF with a probabilistic distribution of product types and offering a new perspective on GCF functions. Lastly, we use BGCat to provide new product class labels for over 100k BGCs in antiSMASH DB that presently have minimal information about their products. AVAILABILITY AND IMPLEMENTATION: The source code and trained model weights are freely available at https://github.com/HassounLab/BGCat.

Multigene Family↗
PubMed2025

Rapid assessment of clinical severity for salmonellosis cases via protein family domain analysis and machine learning.

Salmonella is a common pathogen, infecting more than a million people yearly. Rapid assessment of clinical case severity is essential for improving patient outcomes and optimizing healthcare resources. Advancements in genome sequencing technologies have enabled the analysis of bacterial genomes from many clinical cases, opening up new opportunities for precise and timely diagnosis. This study proposes a genome-based framework for identifying critical Salmonella cases before the onset of critical symptoms and facilitating early medical intervention. By leveraging protein family (Pfam) domains as the representation for genomic data, the complex genetic profiles of Salmonella cases are simplified into interpretable features. The severity levels of cases were investigated through rigorous data analysis, resulting in a set of 70 Pfam domains that could be potentially used as biomarkers. Machine Learning was employed to assess the predictive power of the curated Pfam biomarkers, achieving high accuracy (~93%) in sorting cases into critical, moderate, and mild categories. The results demonstrate the efficacy of the proposed approach. This framework highlights the potential of using bacterial genomic data in clinical decision-making, opening the window for timely personalized interventions for Salmonella infection management.

Domains of unknown function (DUFs)↗
PubMed1997

Classification of protein families and detection of the determinant residues with an improved self-organizing map.

Using a SOM (self-organizing map) we can classify sequences within a protein family into subgroups that generally correspond to biological subcategories. These maps tend to show sequence similarity as proximity in the map. Combining maps generated at different levels of resolution, the structure of relations in protein families can be captured that could not otherwise be represented in a single map. The underlying representation of maps enables us to retrieve characteristic sequence patterns for individual subgroups of sequences. Such patterns tend to correspond to functionally important regions. We present a modified SOM algorithm that includes a convergence test that dynamically controls the learning parameters to adapt them to the learning set instead of being fixed and externally optimized by trial and error. Given the variability of protein family size and distribution, the addition of this features is necessary. The method is successfully tested with a number of families. The rab family of small GTPases is used to illustrate the performance of the method.

Algorithms↗
PubMed2006

Localized brain activation specific to auditory memory in a female songbird.

Song acquisition in songbird males is a prominent model system for the study of the brain mechanisms of memory. Male zebra finches (Taeniopygia guttata) learn their songs from an adult conspecific tutor early in life. Previous work has shown that exposure of males to their tutor song leads to increased expression of immediate early genes (IEGs) in the caudomedial nidopallium (NCM) and in the caudomedial mesopallium (CMM). In addition, IEG expression in the NCM correlates significantly with the strength of song learning. Interpretation of these findings is complicated, as males both learn the characteristics of tutor song and learn to produce a similar own song. Female zebra finches do not sing, but nevertheless they learn the characteristics of a song to which they were exposed when young, and form a preference for it. Here, adult zebra finch females reared with their fathers showed a significant preference for their father's song. Females that were later reexposed to their father's song showed significantly greater expression of Zenk, the protein product of the IEG ZENK, than controls that were exposed to a novel song, in the CMM, but not in the NCM or hippocampus. These results suggest that in female zebra finches the CMM may be (part of) the neural substrate for the representation of the memory of their father's song.

Acoustic Stimulation↗
PubMed1993

Protein structure prediction: selecting salient features from large candidate pools.

We introduce a parallel approach, "DT-SELECT," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-SELECT is able to rapidly choose small, nonredundant feature sets from pools containing hundreds of thousands of potentially useful features. It does this by building a decision tree, using features from the pool, that classifies a set of training examples. The features included in the tree provide a compact description of the training data and are thus suitable for use as inputs to other inductive learning algorithms. Empirical experiments in the protein secondary-structure task, in which sets of complex features chosen by DT-SELECT are used to augment a standard artificial neural network representation, yield surprisingly little performance gain, even though features are selected from very large feature pools. We discuss some possible reasons for this result.

Algorithms↗
PubMed2004

Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition.

MOTIVATION: A key goal of genomics is to assign function to genes, especially for orphan sequences. RESULTS: We compared the clustered functional domains in the SBASE database to each protein sequence using BLASTP. This representation for a protein is a vector, where each of the non-zero entries in the vector indicates a significant match between the sequence of interest and the SBASE domain. The machine learning methods nearest neighbour algorithm (NNA) and support vector machines are used for predicting protein functional classes from this information. We find that the best results are found using the SBASE-A database and the NNA, namely 72% accuracy for 79% coverage. We tested an assigning function based on searching for InterPro sequence motifs and by taking the most significant BLAST match within the dataset. We applied the functional domain composition method to predict the functional class of 2018 currently unclassified yeast open reading frames. AVAILABILITY: A program for the prediction method, that uses NNA called Functional Class Prediction based on Functional Domains (FCPFD) is available and can be obtained by contacting Y.D.Cai at y.cai@umist.ac.uk

Algorithms↗
PubMed2006

Therapeutic targets: progress of their exploration and investigation of their characteristics.

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Adrenergic beta-Antagonists↗
PubMed2004

Consolidation of CS and US representations in associative fear conditioning.

Much attention has been paid to the associative processes that are necessary to fuse together representations of the various components of an episodic memory. In the present study, we focus on the processes involved in the formation of lasting representations of the individual components that make up a fear-conditioning episode. In one-trial contextual fear conditioning experiments, weak conditioning to context occurs if the shock is delivered immediately following placement of the animal in a novel conditioning apparatus, a phenomenon known as the immediate shock deficit. We show that the immediate shock deficit in mice may be alleviated by pre-exposure to either the context or shock. In using this approach to temporally dissect a contextual fear-conditioning task into its constituent representational and associative processes, we are able to examine directly the processes that are important for formation of lasting representations of the context conditioned stimulus (CS) or unconditioned stimulus (US). Our data indicate that the formation of a lasting representation of the context or shock engages protein synthesis-dependent processes. Furthermore, genetic disruption of cAMP-responsive element binding protein (CREB), a transcription factor that regulates the synthesis of new proteins required for long-term memory, disrupts the formation of lasting context memories. We go on to show that the stress hormone epinephrine modulates the consolidation of a context memory, and reverses consolidation deficits in the CREB-deficient mice. Finally we show that disrupting either NMDA or calcium/calmodulin-dependent kinase II (CaMKII) function impairs consolidation of context memories. Together, these data suggest that this approach is particularly suited for the characterization of molecular and cellular processes underlying the formation of stimulus representations.

Animals↗