Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild-type and mutant proteins. This work integrates persistent Laplacians and pre-trained Transformer for the task. The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types. Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%. These alternations have far-reaching implications on the protein’s structure, function, and stability, affecting attributes such as folding stability, binding affinity, and solubility. The consequences of protein mutations have been extensively studied in diverse fields such as evolutionary biology, cancer biology, immunology, directed evolution, and protein engineering . UnderstComputational predictions serve as a valuable complement to experimental mutagenesis analysis of protein stability changes upon mutation. Such computational approaches offer several advantages, including being economical, efficient, and provide a viable alternative to labor-intensive site-directed mutagenesis experiments . As a reOver the years, a variety of computational methods have been developed to explore the effects of mutations on protein solubility, including but not limited to CamSol , OptSolMTopological data analysis (TDA) is a relatively new approach for data science. Its main technique is persistent homology , 13. TheHowever, persistent homology only tracks the changes in topological invariants and cannot capture homotopic shape evolution of data over scales or induced by mutations. To overcome this limitation, Wei and coworkers introduced persistent combinatorial Laplacians, also called persistent spectral graphs, on point clouds  and evolHowever, TDA approaches depend on the biomolcular structures, which may not be available. In fact, many proteins involved in the present study do not have 3D structures. In recent years, advanced natural language processing (NLP) models, including Transformers and long short-term memory (LSTM), have been widely implemented across various domains to extract protein sequence information. For example, Tasks Assessing Protein Embedding (TAPE) introduced three different architectures, namely transformer, dilated residual network (ResNet), and LSTM . AdditioIn this work, we will integrate transformer-based sequence embedding and persistent topological Laplacians to predict protein solubility changes upon mutation. While sequence-based models can be applied without 3D structural information, the PTL-based features require high-quality structures. We generate 3D structures of wild type proteins from AlphaFold2  to facil22.1TopLapGBT integrates both structure-based and sequence-based features, derived from protein structures and sequences respectively, into a unified model. Our architecture comprises three distinct embedding modules: persistent Laplacian-based embeddings, sequence-based embeddings, and auxiliary feature embeddings, all of which feed into an ensemble classifier as depicted in In the persistent Laplacian-based feature embedding module, we employ persistent Laplacian techniques to generate features that encapsulate the structural attributes of proteins both pre- and post-mutation. This approach is particularly effective in capturing the structural alterations induced by mutations within the localized neighborhoods of the mutation sites. Mathematically, the persistent Laplacian builds a sequence of simplicial complexes through a filtration process, thereby characterizing atom-atom interactions across multiple scales , Negative Predictive Value (NPV), Sensitivity, Specificity, Correct Prediction Ratio (CPR), and Generalized Squared Correlation formance . SpecifiThe proposed model, TopLapGBT, demonstrates significant performance gains over existing featurization methods in PON-Sol2 across all evaluation metrics . Specifi2.3To robustly assess the performance of TopLapGBT, we subjected it to an independent test using the same dataset employed by PON-Sol2 . In this3The performance of machine learning models generally relies on the nature of the input features. In our model, the PL-based features depend on one main element which is the quality of the protein structures from AlphaFold 2 (AF2). The quality of AF2 structures are crucial in determining the performance of TopLapGBT. Recently, AF2 structures have been reported to achieve comparable performance to nuclear magnetic resonance (NMR) structures while ensemble methods can be used to enhance the performance by combining multiple NMR structures . This al3.1To delve deeper into the model’s performance, we categorize mutation samples based on their structural regions: interior and surface, as depicted in residues . In our To gain nuanced insights into TopLapGBT’s performance, we segment the results according to the mutation’s structural location within the protein. We present these segmentations as heatmap plots that delineate both mutation regions and amino acid types. Structural regions are defined based on relative accessible surface area (rASA) . By cate3.2Switching focus to mutation types, our model’s capability in classifying solubility changes also merits exploration across the 20 distinct amino acid types in the dataset. In addition to this, we subgroup amino acids as charged, polar, hydrophobic, or special case. Table S1 enumerates the sample counts for each mutation group pair. 3.3The Residue Similarity Index (RSI) serves as a potent metric for evaluating the efficacy of dimensionality reduction in both clustering and classification contexts . RSI hasThe impetus for utilizing structure-based features stems from the multifaceted relationship that exists among protein sequence, structure, and solubility. Factors such as hydrophobicity, charge distribution, and intermolecular interactions contribute to the complexity of protein solubility. Traditional prediction methods, which often rely on empirical rules or rudimentary descriptors, fall short in capturing this intricate molecular interplay. By employing advanced mathematical techniques like persistent Laplacian (PL) coupled with machine learning algorithms, we can decipher the complex patterns and relationships embedded within protein sequences and structures. Persistent Laplacian, in particular, provides a robust mathematical representation that captures both the topological and homotopic evolution of protein structures. Furthermore, machine learning models rooted in advanced mathematics offer several advantages for classifying changes in protein solubility. These models are well-suited for handling high-dimensional and complex data sets, such as those involving protein sequences and structures. They are also capable of learning non-linear relationships and capturing nuanced dependencies that are often overlooked by traditional linear models. Importantly, these advanced models can adeptly manage class-imbalanced datasets, which are commonly encountered in protein solubility studies.4In the multifaceted quest to understand mutation-induced solubility changes, various scientific domains including quantum mechanics, molecular mechanics, biochemistry, biophysics, and molecular biology have made significant contributions. Despite these concerted efforts, state-of-art models have limitations, as evidenced by their normalized CPR value of 0.656 even after employing feature selection methods. Persistent homology (PH) has emerged as a powerful tool for capturing the complexity of biomolecular structures and has achieved noteworthy success in drug discovery applications. However, its inability to capture the nuances of homotopic shape evolution, crucial for delineating molecular interactions in proteins, presents a critical shortcoming.Our study introduces TopLapGBT, a novel model that integrates persistent Laplacian (PL) features with pretrained transformer features, thereby bridging the gap in capturing both topology and homotopic shape evolution. This innovative fusion leads to significant advancements in classification performance. Specifically, TopLapGBT achieves normalized CPR and In summary, our proposed TopLapGBT model not only achieves superior performance over existing state-of-the-art methods but also introduces a more nuanced approach for the classification of protein solubility changes upon mutation. These results underscore the transformative potential of integrating geometric and topological features with machine learning in advancing the field of molecular biology.5In this section, we endeavor to elucidate key mathematical and computational foundations that are instrumental for the work presented in this study. Specifically, we delve into spectral graph theory, simplicial complex, and persistent Laplacian methods, highlighting their significance in capturing topological and spectral properties essential for the characterization of proteins. Additionally, we discuss machine learning and deep learning paradigms, focusing on their role in processing, analyzing, and interpreting these complex features, especially within the confines of test datasets and validation settings.5.1A simplicial complex is made up of a set of simplices and generalises beyond graph networks at higher dimensions , 46, 47.A geometric simplicial complex  complex , 46, 47.The adjoint of For the More specifically, let Then the The number of rows in The above graph Laplacian matrices can be explicitly described in terms of the simplex relations. More precisely, The eigenvalues of combinatorial Laplacian matrices are independent of the choice of the orientation . Further theorem . The kthPersistent Laplacian (PL) were first introduced by integrating graph Laplacian and multiscale filtration . AnalyziTherefore, PL was extended to simplicial complexes. This allows a sequence of simplicial complexes from a filtration process to generate persistent Laplacian which is largely inspired by persistent homology and in earlier works in multiscale graphs. For the rest of this section, we introduce mainly on the construction of PL. First, a A key concept of PL is the filtration process. Essentially, an ever-increasing filtration value arameter . For an This nested sequence of simplicial complexes induces a family of chain complexesIn particular, found in .In order to illustrate the difference between PL and PH, 5.2In order to capture the mutation-induced solubility change, we apply the persistent Laplacian (PL) to characterize the interactions between the mutation site and the rest of the protein. To describe these interactions, we first propose the interactive PL with the distance function The set of persistent spectra from each persistent Laplacian computation consists of For zero dimensions, we consider both the harmonic spectra and non-harmonic spectra information for each persistent Laplacian. Filtration using Rips complex with For one or two dimensions, we perform the filtration using Alpha complex with the ing GUDHI, the per5.3Persistent homology is part of the harmonic spectra of PL. The homology groups in PH illustrate the persistence of topological invariants, hence providing the harmonic spectral information in PL. The site- and element-specific PH features are generated in a similar way as compared to PL. Similar to PL, filtration construction is also employed to PH. For the zero dimension, the filtration parameter can be discretized into several equally spaced bins, namely , (0.5, 1], ⋯, (5.5, 6]Å. The death value of the bars are summed in each bin resulting in For each bin, we count the numbers of persistent bars, resulting in a nine-dimensional vector for each point cloud. Similarly, this is performed for each of the nine single atomic pairs. Hence, the dimension of PH features for a protein is 216. For one or two dimensions, the identical featurization from the statistics of persistent bars in PH is used. The PH embedding combines features at different dimensions as described above and concatenated for wild type, mutant and their difference, resulting in a 648-dimensional vector.5.4Recently, we have seen significant advancements in modelling protein properties using large-scale protein transformer models trained on hundreds of millions of sequences. These models, like ESM  (evoluti5.5PPV and NPV assesses the true positive and true negative proportion of the predicted results for each solubility class. PPV and NPV are computed based on TP, TN, FP and FN which represents the true positive, true negative, false positive and false negative values for each solubility class. For each solubility class, PPV and NPV can be computed by:6Protein sequences are first preprocessed by AlphaFold 2 to generate wild type protein structures. In particular, 3D protein structures are generated from protein sequences using Colab-Fold . Mutant


Here, we represented each protein by ESM-1b and used logistic regression (LR) to train a new model, LR-ESM, for AFP. The experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO 2.0. Therefore, by incorporating LR-ESM into NetGO 2.0, we developed NetGO 3.0 to improve the performance of AFP extensively. NetGO 3.0 is freely accessible at https://dmiip.sjtu.edu.cn/ng3.0.As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins. Recently,  Proteins are complex molecules that play essential roles in various biological activities. To understand the underlying mechanism of an organism as a physical system, annotating the functions of proteins is a crucial task. Gene Ontology (GO) came into being in 1998 to describe varying levels of functional information on gene/RNA/protein, which contains three domains: molecular function (MF), biological process (BP), and cellular component (CC) with over 40,000 terms AFP is a large-scale multi-label classification problem in which multiple related GO terms are assigned to a target protein. In the last few years, several high-performance web servers have been developed for AFP, such as INGA 2.0 e.g., having less than 40 annotated proteins) are excluded in the evaluation, which accounts for around 75% of total annotations in the CAFA setting. In this work, we predicted the associations between proteins and each GO term based on Evolutionary Scale Modeling (ESM)-1b embeddings, which were trained on over 250 million protein sequences Recently, the idea of pre-training in natural language processing A challenging problem is figuring out how to represent protein sequences as fixed-length vectors that capture the realistic sequence–function relationship. Traditional methods rely on a holistic understanding of protein properties. Recently, protein language models have provided a solution that interprets protein sequences as sentences and amino acids as words to extract fundamental features of a protein with rich and systematic information. Protein language models train nonlinear neural networks with an unsupervised objective on a large-scale dataset of protein sequences e.g., contact maps and structural similarity prediction tasks) to enrich protein language models Generally, protein language models apply deep learning models such as recurrent neural networks (RNN) and Transformer to achieve statistical embeddings of proteins from tremendous sequences. UniRep represented protein sequences as fixed-length vectors by long short-term memory (LSTM) with ∼ 24 million sequences In this study, a new component logistic regression (LR)-ESM in NetGO 3.0 was proposed to utilize ESM-1b, a 34-layer Transformer-based model trained on Universal Protein Archive (UniParc) database with 250 million protein sequences and 650 million parameters, to generate protein-level representations by average pooling across all residue-level embeddings NetGO 2.0 integrates seven component methods, which are Naïve, BLAST-KNN, LR-3mer, LR-InterPro, Net-KNN, LR-Text, and Seq-RNN. We replaced Seq-RNN with LR-ESM in NetGO 3.0, which makes function prediction based on a protein language model. Specifically, LR-ESM utilized ESM-1b, a 34-layer Transformer-based model trained on the UniParc database with 250 million sequences As NetGO 2.0 collected the data following the setting of CAFA, we utilized the same benchmark dataset to evaluate the performance of NetGO 3.0 and the competing methods. To take advantage of the latest annotation data, we collected sequences and GO terms before January 2022 from Universal Protein (UniProt) We compared the performance of NetGO 3.0 with the competing methods on the benchmark dataset from NetGO 2.0. The performance was evaluated by area under the precision–recall curve (AUPRC) and two standard metrics in CAFA, the maximum F1-score  is a difficult protein with low BLAST similarity to training proteins. We have developed NetGO 3.0 to improve the performance of large-scale AFP by incorporating a new component LR-ESM, which utilizes a protein language model to generate powerful representations of proteins. Interesting future work would be integrating protein structural information into NetGO 3.0 to enhance the performance of AFP https://dmiip.sjtu.edu.cn/ng3.0.The web server of NetGO 3.0 is freely accessible at The authors have declared no competing interests.Shaojun Wang: Data curation, Software, Methodology, Writing – original draft. Ronghui You: Conceptualization, Methodology, Writing – review & editing. Yunjia Liu: Methodology, Visualization, Writing – review & editing. Yi Xiong: Resources, Writing – review & editing. Shanfeng Zhu: Conceptualization, Resources, Methodology, Writing – review & editing. All authors have read and approved the final manuscript.


Dengue Fever (DF) is a vector-borne neglected viral disease with a high burden in the sub-tropics of Asia and Africa. Aedesaegypti is responsible for 90% of cases in the global burden of disease. The primary goal of the treatment is to eliminate the virusfrom the bloodstream of affected individuals. A successful dengue vaccine must elicit both neutralizing antibodies and cell-mediatedimmunity and there is no vaccine to date to prevent DF. A multi-epitope vaccine composed of a series of or overlapping peptides is,therefore, an ideal approach for the prevention and treatment of pathogenic organisms. An immunoinformatics approach was employed todesign a theoretical multi-epitope vaccine candidate. This vaccine candidate consists of linear B-cell epitope, TH cells epitope andCTL of reported potential vaccine candidates. These epitopes were linked together with suitable linkers and adjuvant at the Nterminal and C terminal. The 3D Structure of the vaccine was modeled, refined and validated using computational tools.Protein-protein docking of vaccine candidates with TLR3 protein results in efficient binding. Immune stimulation of vaccinecandidates predicted high levels of IgG and IgM. This candidate vaccine should be validated experimentally using suitable in-vivoand in-vitro studies to use in dengue fever virus elimination programmes. Dengue fever is a mosquito-borne tropical disease caused by the dengue virus. Dengue virus is transmitted by female mosquitoesmainly of the species Aedes aegypti and, to a lesser extent, Ae. albopictus. Dengue has become a global burden since the SecondWorld War, mainly in South Asia. About 390 million people are infected a year and approximately 25,000 people die .Dengue Proteins were taken from UniProtKB: The UniProt Knowledgebase (UniProt ID: A0A2P0X374). The overall workflow of this study isgiven in BepiPred-2.0 is a web server for predicting B-cell epitopes from antigen sequences. It is based on a random forest algorithmtrained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other availabletools for sequence-based epitope prediction both on epitope data derived from solved 3D structures and on a large collection oflinear epitopes downloaded from the IEDB .NetCTL is a web-based tool designed for predicting CTL epitopes. It does so by integrating predictions of proteasomal cleavage,TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attemptto predict CTL epitopes: EpiJen, MAPPP, MHC-pathway and WAPP. In order to compare the performance of prediction methods, objectivebenchmarks and standardized performance measures are needed. NetCTL works with large-scale benchmarks and corresponding performancemeasures and reports the performance of an updated version 1.2 of NetCTL in comparison with the four other methods. The predictionmethod integrates prediction of MHC class I binding peptides, proteasomal C-terminal cleavage, and TAP (Transporter Associated withAntigen Processing) transport efficiency. Though the server allows for predictions of CTL epitopes restricted to 12 MHC class Isuper types .There are two versions of MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructedusing an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database coveringHLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules using artificial neural networks. The prediction of MHC II epitopes was based onreceptor affinity, which can be inferred from the IC50 values and percentile ranks assigned to each predicted epitope. High-affinitypeptides should have IC50 values <50nM. An IC50 value <500nM indicates intermediate affinity, while values <5000nM indicate lowaffinity.Predicted linear B-cell epitopes, high scoring CTL epitopes and high affinity TH epitopes were used to construct multi-epitopevaccine candidate sequence. The TH and CTL epitopes were linked using GPGPG  and AAYANTIGENpro is a sequence-based, alignment-free, and pathogen-independent predictor of protein antigenicity. The predictions aremade by a two-stage architecture based on multiple representations of the primary sequence and five machine learning algorithms. Afinal SVM classifier summarizes the resulting predictions and predicts if the protein is likely to be antigenic or not as well asthe corresponding probability. ANTIGENpro is the first predictor of the whole protein antigenicity trained using reactivity dataobtained by protein microarray analysis for five pathogens. AllerTop v.2.0 reported a set of novel models for allergen predictionutilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods forclassification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron(MLP) and k nearest neighbour (kNN). The best performing method was kNN with 85.3 % accuracy at 5-fold cross-validation. Theresulting model has been implemented in a revised version on the AllerTop server .Various physicochemical properties for the designed protein were predicted using web server ProtParam(https://web.expasy.org/protparam/). ProtParam is a tool which allows the computation of various physical and chemical parametersfor a given protein stored in Swiss-Prot. The computed parameters include the molecular weight, theoretical pI, amino acidcomposition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index and grand averageof hydropathicity (GRAVY).Self-optimized prediction method (SOPM) has been described to improve the success rate in the prediction of the secondarystructure of proteins. SOPMA was used for secondary structure prediction of vaccine sequences.Ab initio- or de novo- protein modelling methods seek to build three-dimensional protein models "from scratch", i.e., based onphysical principles rather than (directly) on previously solved structures. Robetta is a server that provides automated proteinstructure prediction and analysis capabilities. Sequences are processed into putative domains and structural models are constructedusing either comparative modeling or de novo structure prediction approaches for structure prediction. If BLAST, PSI-BLAST, or3D-Jury finds a confident match to a known structural protein, it is used as a template for comparative modelling. If no match isidentified, the de novo Rosetta fragment insertion approach is used to make structural predictions. Data from experimental nuclearmagnetic resonance (NMR) constraints can also be submitted using a RosettaNMR de novo structure query sequence. Other existingcapabilities include predicting the effects of mutations on protein-protein interactions using the computational interface alaninescanning. In the near future, Rosetta's protein design and protein-protein docking technologies will be made available via theserver .CLUSPRO server provides a simple home page for basic use, requiring only two files in Protein Data Bank format. However, ClusProalso offers a number of advanced options to modify the search that include the removal of unstructured protein regions, applyingattraction or repulsion, accounting for pairwise distance restraints, constructing homo-multimers, considering small angle X-rayscattering (SAXS) data, and finding heparin binding sites. Six different energy functions can be used depending on the type ofproteins. Docking with each energy parameter set results in ten models defined by centres of highly populated clusters of lowenergy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files,the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generallycompleted in < 4 hours .To further characterize the immunogenicity and immune response profile of multi-epitope vaccine candidates, in silico immunesimulations were conducted using the C-ImmSim server (http://150.146.2.1/C-IMMSIM/index.php?page=1). C-ImmSim is an agent-basedmodel that uses a position-specific scoring matrix (PSSM) for immune epitope prediction and machine learning techniques forprediction of immune interactions. It "simultaneously simulates three compartments that represent three separate anatomical regionsfound in mammals: (i) the bone marrow, where hematopoietic stem cells are simulated and produce new lymphoid and myeloid cells;(ii) the thymus, where naive T cells are selected to avoid auto immunity; and (iii) tertiary lymphatic organ, such as a lymph node".The complete amino acid sequences of the E protein of Denv type 2 was retrieved from UniProtKB (https://www.uniprot.org/) inFASTA format. E protein, the smallest outer surface protein from Denv type 2 genome, was found to possess the highest antigenicityand is therefore used to identify B-cell and T-cell epitopes. E-protein can therefore be targeted as a potential vaccine targetagainst Denv type 2 .Epitope-based vaccines represent a novel approach for generating a specific immune response and avoiding responses against otherunfavourable epitopes (like epitopes that may drive immunopathogenic or immune modulating responses) in the complete antigen. Thiswork therefore focused on the in-silico design and development of a potential multi-epitope vaccine peptide for dengue fever virususing E protein sequence. The dengue viral E protein is responsible for cell receptor binding and it is the main target forneutralizing antibodies. Therefore, the dengue E protein is an important antigen for vaccine development and diagnostic purposes.Selected protein sequences are subjected to B cell prediction (BepiPred-2.0 web server) ,CytotoxAb-initio model for multi-epitope vaccine construct was modeled using robetta server. It gave five models as a result. The 3Dstructure of the vaccine candidate improved markedly after the refinement and showed desirable properties based on Ramachandran plotpredictions. The Ramachandran plot shows 80.4% in most favoured regions, 0.9% in generously allowed and 0.3% disallowed regions;this indicates that the quality of the overall model is satisfactory. Protein-protein docking was performed and it shows goodbinding affinity with TLR3 (PDB ID: 2A0Z) was performed using cluspro 2.0. The predicted tertiary structure is given inThe docked complex model 1 has the lowest energy score (-1078.0kJ/mol) the highest binding affinity, thus was selected as thebest-docked complex. Immune simulation showed results consistent with typical immune responses. C-ImmSim server immune simulationyielded results consistent with actual immune responses. In denv IgG and IgM responses to viral antigens are implicated in diseaseprotection. The development of memory B-cells and T- cells was evident, with memory in B-cells lasting several months. Helper Tcells were particularly stimulated. Neutralizing monoclonal antibodies is designed by using these linear and conformational epitopesagainst dengue virus that can work as an effective vaccine to save many precious lives.The elimination of Dengue fever virus will not be achieved without novel control methods. These involve diagnostic andtherapeutic tools as well as a vaccine if possible. In this study, immunoinformatics tools were employed to design a potentialvaccine peptide coding for multiple B-cell and T-cell (TH and CTL) epitopes. Given that the proteins containing these epitopescould potentially provide both prophylactic and therapeutic benefits. This vaccine peptide could potentially be used as acomplementary tool to achieve dengue fever virus elimination with further suitable in vivo and in vitro studies.


The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field. Over the past years, artificial intelligence (AI) and machine learning (ML) approaches have increasingly found their way into biomedical research, healthcare applications, and drug discovery. AI augments the capabilities of human researchers, often outperforming alternative methods, and leverages large amounts of high-quality data obtained in biochemical and biophysical experiments for model training which is a key bottleneck that can unlock the full potential of AI technologies. The advances in the ML field have driven the design of new tools and systems to model increasingly complex chemical and biological phenomena ,2,3,4,5.Structural and biophysical studies of allosteric molecular events that involve the formation of short-lived allosteric states and intermediates have benefited from advances in nuclear magnetic resonance (NMR) technologies that can be employed as powerful tools for probing of allosteric interactions and communications in proteins ,16,17,185 combinations of mutations on antibody neutralization [Recent biochemical studies exploited and further advanced the Deep Mutational Scanning (DMS) approach to enable direct mapping of residue-based biophysical effects of mutations on protein stability, binding, and expression  37,38,3,47,48,49lization . A pioneering approach for global mapping of the energetic and allosteric landscapes of protein interaction domains leverages the massively parallel nature of DMS and employs multidimensional mutagenesis to causally quantify the phenotypic effects of thousands of perturbations for multiple molecular phenotypes in diverse genetic backgrounds . The newIn a very recent follow-up study, Lehner and coworkers developed a comprehensive map of allostery and reported multiple global atlases of the inhibitory allosteric communications in KRAS protein by measuring the impact of mutations on the protein folding and binding to six interaction partners . By usinDMS studies of the FabZ, LpxC, and MurA proteins that are involved in cell envelope synthesis were performed with the aid of high-throughput CRISPR genome editing, revealing the effect of >17,000 protein variants on function . MutatioA high-throughput platform was developed for DMS analysis of caspase-3 (CASP3) and caspase-7 (CASP7) allowing for a large-scale assessment of mutations on caspase structure and function and revealing differences between CASP3 and CASP7 proteolytic activity and protein stability . This stAllosteric mapping of GTPase switch Gsp1/Ran system in the context of its in vivo protein interactions by comprehensive mutational scanning enabled characterization of allosteric sites for highly regulated molecular switches that catalyze GTP hydrolysis and nucleotide exchange . Using aML models leveraged the data-rich DMS results to generate predictive models and obtain mechanistic insights into genotype-phenotype maps ,59,60,61Multiplex assays of variant effect (MAVEs) are a type of DMS experiment that utilize a combination of reporter assays, protein expression assays, and next-generation sequencing (NGS) to screen thousands of variants in a single experiment and can be used to study the effect of genetic variants on gene expression, protein stability, and protein-protein interactions. Quantitative modeling of MAVE data is based on the use of latent phenotype models, which describe the relationship between the observed data and a latent phenotype. The framework is designed to capture the complex structure of MAVE data, including both the observed phenotype data and the underlying latent phenotype. The latent phenotype model is then used to estimate the latent phenotype from the observed data, and to quantify the effect of different environmental factors on the underlying latent phenotype . Using tExploring and learning the protein allostery universe becomes increasingly plausible using the proliferation of AI/ML tools and methods to solve long-standing problems in molecular biology and biophysics. The remarkable success of AlphaFold2 (AF2) in protein structure modeling and looming resolution of the protein folding problem in the last few years have marked a revolutionary change in structural biology that was inconceivable even several years ago ,65. HoweThe biological Importance and complexity of protein conformational dynamics and allosteric mechanisms require next-generation computational approaches and innovative AI-based integrative tools synergizing advances in computation and experiments that can enable atomistic characterization of allosteric landscapes, states, and interactions. Development of AI-augmented simulation approaches that leverage ML tools to represent physics-based thermodynamic and kinetic determinants of conformational sampling as deep neural networks has gained a significant momentum in recent years. ML approaches were employed to facilitate exploration of conformational landscapes using MD simulations via optimal selection of reaction coordinates. By combining deep learning (DL) approaches and biased MD simulations, physically meaningful collective variables can be determined to resolve the bottlenecks that often hinder the reliable characterization of conformational transitions and rare events . UtiliziVAMPNets, an end-to-end learning system, can be used to predict molecular kinetics with neural networks . The comThe nature and atomistic details of the allosteric communication between the allosteric site and the functional site are often difficult to dissect. While experimental approaches could reveal allosteric hotspots and potential communication pathways in protein structures, robust and large scale-based tools for mapping of allosteric communications and allosteric binding sites are still missing. The problem of identifying putative allosteric sites can be addressed by considering the communication with orthosteric sites and uncovering the allosteric communication pathways between orthosteric and allosteric sites. Computational methods for predicting binding sites and allosteric pockets can be classified as geometric, energetic, evolution-based, knowledge-based, and machine learning approaches. AlloPred uses normal mode perturbations along with pocket descriptors in a machine learning approach to rank allosteric sites, with a good performance of 28 out of 40 cases . The AllRecently, autonomous predictors of binding sites based solely on ML methods have emerged, replacing traditional template-free methods such as Fpocket , SiteHouIn recent years, the concept of end-to-end learning has been gaining attention. This approach involves three main steps: (a) reducing the dimensions of inputs, (b) creating various neural networks based on the available data, and (c) applying backpropagation across the entire architecture to reduce loss and update weights. For example, a neural relational inference model that utilized a graph neural network and an autoencoder architecture was used to explore the latent embedding of allosteric systems, learn about the long-range interactions and communication between distant sites in the ligand-induced allosteric regulation of Pin1, conformational transition of SOD1 protein, and the activation of MEK1 caused by oncogenic mutations . AnotherA template-free P2Rank approach is among the most efficient and fast available ML tools for prediction of ligand binding sites ,105. It Recent advances in convolutional neural networks (CNNs) have made them very effective in image recognition tasks. They have been widely applied to detect protein binding sites which are represented as 3D-voxels with atomic attributes. A novel, rapid, and accurate DL approach, BiteNet  was developed for large-scale and spatiotemporal identification of protein binding sites. Drawing inspiration from computer vision problems such as object detection in images and video, BiteNet considers protein conformations as three-dimensional images with channels corresponding to the atomic densities, binding sites as the objects to detect on these images, and conformational ensembles of proteins as three-dimensional videos to analyze . In compAn ensemble learning method combining eXtreme gradient boosting (XGBoost) and graph convolutional neural networks (GCNNs), and an automated machine learning method (AutoGluon and AutoKeras) was developed to predict plausible allosteric sites and both models were incorporated to Prediction of Allosteric Sites Server (PASSer)  108,109109. MostCryptic binding sites are small and hidden pockets in proteins that are difficult to detect using traditional methods such as X-ray crystallography and NMR spectroscopy as they may emerge in meta-stable intermediate states that are typically short-lived and dynamic in nature, often appearing during allosteric conformational changes. Additionally, cryptic pockets may contain non-polar or hydrophobic residues that do not form strong hydrogen bonds with other molecules in the protein, making them difficult to detect by computational methods. Accurate and rapid discovery of potential cryptic pockets in a protein structure is an important and challenging problem and robust approaches capable of uncovering hidden druggable sites that can greatly accelerate the scope of therapeutic interventions and modernize drug discovery of the future. While the repertoire of available methods for detection of protein binding pockets and ligand binding sites is rich and diverse, the reliable identification of allosteric binding sites and hidden allosteric pockets available in short-lived intermediate states is far more challenging. The emergence of recent AI and ML models to incorporate the elements of protein dynamics and sophisticated CNN and DL architectures may provide a fruitful avenue for future developments. These directions are organically linked with the increasing interest to adaptation of AF2 tools for prediction of conformational ensembles and functional states with previously unknown hidden pockets making them an appealing target for a new generation of drug discovery technologies. An important step forward to address challenges of detecting cryptic binding pockets emerging from conformational ensembles is made in a novel method PocketMiner that employs DL-based architecture to identify pockets in large protein structures that have not been identified through crystallography or other experimental techniques. In contrast to previous methods, PocketMiner utilizes molecular simulations to evaluate if each residue in a protein structure has the ability to rearrange its orientation to participate in a cryptic pocket as part of its thermal fluctuations. The proposed DL model is trained on extensive datasets of structural ensembles derived from simulations that contain examples of pocket opening events. PocketMiner can detect hidden binding sites more than 1000 times faster than existing methods when applied to single structures from a newly created dataset of 39 experimentally confirmed cryptic pockets ,112. PocRecent advances in single-particle cryo-electron microscopy (cryo-EM) have enabled the determination of numerous near-atomic resolution structures for well-ordered proteins and large macromolecular assemblies breaking resolution barriers for studies of allosteric events and allosteric drug discovery ,114,115.The recent development of data processing capitalizes on AI and ML models to enhance the efficacy of data analysis and validation and automate the steps of particle picking, 3D map reconstruction, and local resolution determination . FurtherSingle-molecule Förster resonance energy transfer (smFRET) paved the way for studying dynamics in macromolecular structures under biologically relevant conditions  The receMicrosecond MD simulations have been used in combination with ML methods and restraints derived from smFRET experiments to successfully recapitulate and interpret smFRET states and allosteric conformational transitions ,128,129.The DeepFRET method utilizes DL architectures for rapid and automated classification of smFRET data, which is a significant challenge in smFRET experiments and the analysis of protein dynamics . This apBy leveraging smFRET and MD simulations, researchers have explored the molecular basis and dynamic events associated with allosteric regulatory mechanisms. For example, a study of the NMDA receptor uncovered the mechanism of allostery underlying negative cooperativity, revealing that binding of one agonist leads to flexibility and increased conformational spread at the second agonist site . AdditioBy combining smFRET, fluorescence correlation spectroscopy (FCS), and fluorescence lifetime measurements with atomistic MD simulations, researchers were able to show how ATP hydrolysis in Hsp90 chaperones induces allosteric changes at a distant protein binding site and modulates chaperone activities . FurtherDespite considerable progress in applications of AI and ML methods in cryo-EM and smFRET studies, the intersection between allosteric regulation, the emerging structural biology technologies, and AI approaches are largely unexplored, calling for the development of AI-augmented integrative structural biology. This is an exciting area of research, with the potential to reveal the underlying mechanisms of allosteric regulation and to develop new ways of controlling the behavior of proteins. By better understanding the mechanisms of allosteric regulation and developing new AI approaches, we can gain a better understanding of how proteins work and how they can be manipulated to produce desired biological outcomes. In addition, by combining these technologies, AI-enabled integrative tools could unveil the invisible aspects of protein ‘life’ revealing hidden protein states and reconstructing allosteric landscapes for therapeutically important regulatory proteins operating under allosteric control. Through potential synergies of these innovative technologies, AI-enhanced biochemical and structural biology tools can create novel ways of controlling the structure and activity of proteins to achieve desired outcomes, leading to a new era of protein engineering and synthetic biology.Despite the accepted understanding that intricate protein systems and regulatory complexes often function as dynamic and diverse allosteric machines, the characterization of hidden and uncommon protein functional states, allosteric structural changes, and allosteric pathways is still surprisingly scarce, necessitating the combination of new structural, biophysical, and computational approaches to address these problems. Research in the area of allosteric mechanisms is advancing in several new directions, such as the use of computational methods for the detection and visualization of allosteric networks, as well as the development of novel experimental methods to study allosteric processes. The latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric mechanisms using novel experimental technologies created a wealth of high-quality data that were used in the development of new experiment-guided AI systems. The recent studies have shown that the continuous developments and adaptations of AI and ML tools in high throughput biochemistry and their integration with DMS tools can reveal atomistic details of allosteric landscapes and result in a comprehensive characterization of allosteric mutations, yielding unprecedented insights into molecular mechanisms of complex biological systems.The latest advances in structural characterization of allosteric molecular events and hidden functional states using cryo-EM, NMR, smFRET spectroscopy have highlighted the growing need for data-centric integrative biophysics approaches. These emerging methods and tools, including high-throughput sequencing, microscopy imaging, proteomics, metabolomics, and systems biology, involve utilization of AI and ML models trained on large datasets from different sources. AI-augmented integrative biophysics approaches leverage the power of AI to facilitate the integration of multiple data sources and to discover novel physical principles underlying biological systems. The latest developments reviewed in this study suggest that AI can be used to develop more accurate predictive models of diverse and complex biological systems, identify relevant features from large scale datasets, and provide insights into the underlying physical mechanisms of biological processes. AI-augmented biophysics approaches in structural biology can also be used to automate the experimental design process and optimize laboratory techniques, allowing scientists to conduct research and identify meaningful patterns in the data quickly and efficiently. In addition, AI-augmented integrative biophysics approaches will provide new strategies for drug design and development—from allosteric drug discovery to pathway-targeted design, systems medicine, and protein engineering applications. By developing an open science infrastructure for machine learning studies of allosteric regulation and validating computational approaches using integrative studies of allosteric mechanisms, the scientific community can expand the toolkit of approaches for dissecting and interrogation allosteric mechanisms in many therapeutically important proteins. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by a booming AI field.


In a post-AlphaFold2 world, accurate models of tertiary structures are widely available for even more protein targets, so there has been a refocus in the prediction community towards the accurate modelling of protein-ligand interactions as well as modelling quaternary structure assemblies. In this paper, we describe the latest improvements to IntFOLD, which maintains its competitive structure prediction performance by including the latest deep learning methods while also integrating accurate model quality estimates and 3D models of protein-ligand interactions. Furthermore, we also introduce our two new server methods: MultiFOLD for accurately modelling both tertiary and quaternary structures, with performance which has been independently verified to outperform the standard AlphaFold2 methods, and ModFOLDdock, which provides world-leading quality estimates for quaternary structure models. The IntFOLD7, MultiFOLD and ModFOLDdock servers are available at:   The routine use of highly accurate predicted protein tertiary structure models by life scientists continues to grow, particularly now that we are in a post-AlphaFold2  world. HIntFOLD has undergone major modifications to the underlying component methods, which have led to significant performance gains since our last paper describing the server, in 2019 . IntFOLDIn the CASP15 experiment, there was an additional focus not only on modelling protein-ligand interactions, such as those provided by the IntFOLD7 server but also on modelling protein-protein interactions. In light of this, we have also developed our two new complimentary servers to specifically address the problems of both modelling quaternary structures (MultiFOLD) and scoring the resulting multimeric models (ModFOLDdock).MultiFOLD was found to be highly competitive in the CASP15 experiment, ranking among the top ten servers on both the regular monomeric and the multimeric targets. Furthermore, ModFOLDdock was found to be a leading server for estimating the quality of the quaternary structure models. Scoring the quality of 3D models has always been a critical aspect of protein structure prediction pipelines, however, until now there have been few available methods for reliably and independently scoring modelled protein complexes. ModFOLDdock fulfils this pressing need, which is becoming more pertinent as alternative deep learning multimer modelling methods continue to emerge. Following the rigorous independent evaluation by the CASP15 assessors, we have now made the MultiFOLD and ModFOLDdock servers freely available, providing them with intuitive web interfaces to benefit non-expert predictors in the wider bioscience community.https://predictioncenter.org/casp15/).For brevity, below we summarise the core aspects of the methodology behind the IntFOLD7, MultiFOLD and ModFOLDdock servers. For further information and details of their specific parameters, please refer to our previous papers, CASP15 abstracts and presentations  model quality estimates. There are 3 variants of the ModFOLDdock method which use various combinations of scores, which are calculated using the output from 7 individual scoring methods: DockQJury, QSscoreJury QSscoreOfficialJury, lDDTOfficialJury, voronota-js-voromqa , CDA 5,,5,12 andThe only required input for the IntFOLD7 server is a single amino acid sequence in single letter format. However, optionally users may provide a short name for their prediction job and an email address, which will only be used to provide a notification of the link to their results once their predictions are completed. If users do not wish to provide an email address, then they can simply bookmark the link to the results page for later viewing. The prediction data are presented graphically, and models are viewable interactively in 3D. Machine-readable data are also provided which comply with CASP data standards.Examples of the graphical outputs from the IntFOLD7 server are shown in Figure The MultiFOLD server requires users to provide their input sequences for the target proteins in FASTA format. Optionally, users may also provide a short name and email address if they wish to receive a notification or they can just bookmark the link to the results pages, which is provided after submission. It is recommended that users also provide stoichiometry information for their complex if this is known. If the stoichiometry is unknown, then MultiFOLD will attempt to predict it from known template structures.The resulting models of protein complexes can be viewed interactively in 3D directly within a standard desktop or mobile web browser . The resFor the ModFOLDdock server, the user-required inputs for the server are the input sequences for the target proteins provided in FASTA format, the stoichiometry of the target complex, and a single 3D model for evaluation. Optionally, users may upload multiple alternative models, a name for their protein sequence and their email address. Users of the server may also select from three different variants, which use different combinations of component scoring methods optimised for the different facets of the quality estimation problem. Firstly, ModFOLDdock, with predicted global scores that are optimised to provide positive linear correlations with observed scores. Secondly, ModFOLDdockR with global scores optimised for ranking the best models at the top. And finally, ModFOLDdockS, for scoring single models at a time.The results table ranks models according to the predicted global assembly and interface quality scores . As withhttps://www.cameo3d.org/), where IntFOLD7 ranks as one of the leading freely publicly available methods.IntFOLD has undergone successive iterative improvements, which have been independently tested in each of the relevant categories of the CASP experiments. IntFOLD7 ranked competitively in the recent CASP15 experiment, particularly in the Interdomain category where it outperformed many leading human predictor groups. In addition, the predictions are also continuously evaluated by the CAMEO project . Additionally, we made extensive use of the integrated binding site predictions, from the FunFOLD component of IntFOLD7, to submit our manual predictions for the new ligand category.In the CASP15 experiment, we relied on our IntFOLD7, MultiFOLD and ModFOLDdock servers to help us with our manual predictions. Overall, our McGuffin group ranked sixth in the regular tertiary structure prediction category and 11th in the multimeric target prediction category, according to the default rankings  in the interdomain  and reguThe MultiFOLD server ranked even higher as the ninth best server group on monomeric targets according to the GDT_TS scores  and the Furthermore, in the CASP15 Estimates of Model Accuracy category, the ModFOLDdock server variants were ranked within the top few groups by all combined measures for the global (fold) scores, the global (interface) scores and the local mode scores  and continuous benchmarks (CAMEO), while our ModFOLDdock server is one of the top few leading methods for estimating the quality of modelled complexes. Following this independent evaluation, we are now also making MultiFOLD and ModFOLDdock freely available, providing intuitive web interfaces to benefit the wider bioscience community.The data underlying this article are available in the article and in its online supplementary material.gkad297_Supplemental_FileClick here for additional data file.


Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models. The extraordinary adaptability of these proteins enables them to maintain functionality even in extreme conditions— a feat that unfortunately remains a formidable challenge in protein design and engineering8. One key determinant of protein adaptability is thermal stability, governed by the Gibbs free energy difference between the folded and native states. This energy balance, crucial for proteins to maintain their functional conformation, remarkably holds across various organisms and environments10. Even so, minor alterations in protein sequences can have profound impacts on their intricate structures, and consequently, on their stability and folding capacity11. Despite these challenges, life finds a way: thermophiles, which rely on HTPs, can thrive at extreme temperatures15.High-temperature proteins (HTPs) find their way into wide-ranging applications spanning from drugs and diagnostics in human health to catalysing reactions in industrial processes and bioremediation24. Recent advances in fully deep learned designers have shown impressive results and generating sequences that reliably fold to a target structure, particularly at ambient temperatures28. While new and improved models are continuing to be developed, they do not invariably produce designs that retain tertiary structure and activity at high temperatures31. Strategies which are based on substructures, energy functions, or patterns learned from protein structures represented in the PDB, are limited considering that the majority of proteins are non-thermophilic: only 5% of proteins from the top 25 most populous source organisms are thermophilic35. The temperature-dependent nature of enthalpic and entropic forces in the protein means that stability at ambient temperature does not necessarily translate to high-temperature stability37. Learning from high temperature proteins in a targeted manner may further improve reliability of novel methods. Consequently, researchers have been in search of rules employed by nature to produce HTPs for years42. Table Current methods that aim to produce proteins functional at higher temperatures, such as directed evolution or rational design, unfortunately, provide no guarantee of success and require substantial effort for each new protein of interest44. Larger datasets that label substantial portions of proteins using parameters such as melting temperature (TM) or the more noisy optimal growth temperature (OGT) of the host organism can be used to discern average trends48. However, any thermal stability modes extracted in this way may not be universally applicable, as thermal stability is often specific to the protein fold itself50. Therefore, patterns identified on average may even prove destabilising when applied to a new target51. To account for this, thermal stability should be studied in a context dependent manner, where homologs are paired across temperature, with multiple examples across evolution. The largest dataset of protein pairs contains only 1.6k unique examples, and does not have redundancy across evolution52.Existing datasets, apart from their size, suffer from a number of limitations. For example, single point mutation datasets such as FireProtDB allow for the study of stability independent from confounding factors like evolutionary drift, yet they offer limited variety and informational density when compared to the variety of known proteins58. Each of these applications, however, is predicated on large (>10k) datasets. To translate these successes to context-dependent thermal stability design, the development of a large dataset of homologous protein pairs across different temperatures becomes imperative. This work introduces such a dataset: learn2thermDB. The dataset includes 69 million protein pairs of 250 amino acids or fewer derived from 4739 mesophilic organisms (with OGT < 40 °C) and 289 thermophilic organisms  using homology search. We first paired mesophilic organisms to thermophilic ones by evolutionary distance, and then homologous proteins among those taxa pairs. Applying a stricter thermophilic threshold of 60 °C, used by the current largest dataset, our database still retains 9 million protein pairs, a four orders of magnitude increase. It is worth noting that individual proteins often participate in multiple pairs, providing the database with some redundancy across the evolutionary landscape. Moreover, even those organisms that did not demonstrate pairing still contribute to the dataset with their proteomes labelled by OGT, yielding 23 million proteins from mesophiles and 1 million from thermophiles (with a maximum OGT of 102 °C) for further study. The dataset’s size, in terms of the included organisms and proteins, is depicted in Table et al. is also depicted for comparison in Fig. 59.Recent advancements have shown that large deep learning models can effectively understand protein context in applications such as binding predictions, structure prediction, and backward design, among othersn large >0k datasen large >0k datasen large >0k dataseIn addition to the static dataset provided, the entire pipeline used to produce the data is not only open source, but parameterized, such that an interested reader can reproduce or alter the produced dataset. Please see section “Usage Notes.” We believe that the carbon cost of compute efforts is as important as the result itself, thus our pipeline tracks approximate carbon cost . The cost of running the pipeline was estimated to be 11.5 kg carbon, according to the energy partition in Washington State, USA, and the compute total carbon cost of this research effort including development was estimated at about 70 kg.Our process of identifying homologous protein pairs across temperature is discussed in the following sections. The pipeline is depicted in Fig. 61. We only retained full sequences, ranging from 1300 to 1600 bases. When multiple sequences mapped to the same taxid, we observed >98% identity and retained the longest. Curated OGTs, produced by Engqvist, were then downloaded63. For cases with multiple OGTs corresponding to the same species taxid, we used the average. The OGTs and 16s rRNA sequences were subsequently linked via taxid, generating a table with both quantities measured. For the next steps, we categorised organisms with OGT > 40 °C as thermophiles. It should be noted that this information is tracked, enabling the users to filter data using a more stringent thermophilic label, such as 60 °C3.We retrieved Archaeal and Bacterial 16s rRNA sequences, along with their associated NCBI taxonomy identifier (taxid) from NCBI BioProjects 33175 and 33317 using the Entrez API64. UniProtKB Proteome metadata was also retrieved to minimise redundancy while preserving taxonomic diversity. For strains with multiple proteomes, if any, we selected a single proteome based on UniProt’s priority labels: “Reference and representative proteome”, “Reference proteome”, “Representative proteome”. If these labels were absent, we discarded “Redundant proteome” and “Excluded proteome”, choosing the most populous remaining proteome. This process was repeated at the species level. Protein data files were parsed for primary sequence, host organism taxid, PDB, and AlphaFold database ID. Proteins not mapping to an organism in the taxa table were discarded. If a protein mapped to a proteome, it was retained if it belonged to the priority proteome identified for the organism, or if the priority proteome for the organism contained fewer than 2000 proteins. This avoided dataset saturation with model organism proteins while still including novel or infrequently studied proteins.We downloaded Archaeal and Bacterial protein sequences from UniProtKB65. The search space, considering only proteins <250 amino acids, was approximately 4.5 trillion pairs. A full pairwise BLAST-like search given the protein data space was projected to cost an unacceptable amount of carbon at 50,000 kg. To mitigate this, we first filtered the search space by identifying evolutionarily related taxa across temperatures. Using BLASTn, we aligned 16s rRNA sequences for mesophilic and thermophilic organisms. This sequence evolves slowly and is frequently used for inferring taxonomic relationships68. See Supplementary Information To provide context-dependent information on protein thermal stability, pairs of proteins must be identified from a large pool of thermophilic and mesophilic ones. To do this we identified homologous sequences using a BLAST-like local alignment69. A resource test indicated that DIAMOND had a twofold speed increase, while preserving sensitivity , is tracked using data version control (DVC). Thus, the complete history of how the data changed as the code was developed and the parameters were changed  is available. Additionally, the pipeline can be rerun with a single command after environment and computing cluster configuration. The data tables created in this process  are collected and linked as a relational database using DuckDB, allowing for fast access and filtering of the data59 in the form of a relational database, as well as a dump of semicolon separated value files.The dataset is available on Figshare70. It consists of four main tables: ‘taxa’, ‘proteins’, ‘taxa_pairs’ and (protein) ‘pairs’. An abbreviated schema detailing the relationships between these tables is illustrated in Fig. The dataset is structured as a relational database, implemented with DuckDB, an analytical database management systemFor 16s rRNA alignment (table “taxa_pairs”) and protein alignment (table “pairs”) conducted using DIAMOND, various metrics including bit score, coverage, etc. are reported and referenced throughout the manuscript. Detailed definitions of these metrics are provided in Supplementary Information 59. The dataset can be found in a zipped file named “database.tar.gz.” This file includes the database “learn2therm.ddb”, a minimal environment file for querying the database called “enviroment.yml,” and a set of instructions provided in the “README.md” file. The database employs SQL, with some specific elements defined by DuckDB. For example, to retrieve all protein pairs and their corresponding amino acid sequences with >95% alignment coverage of both strands and thermophilic temperature >80 °C, use the following query:To access the dataset, refer to the provided link on FigshareSELECT m.protein_seq AS meso_seq, t.protein_seq AS thermo_seqFROM pairsINNER JOIN proteins AS m ON (m.pid = pairs.meso_pid)INNER JOIN proteins AS t ON (t.pid = pairs.thermo_pid)INNER JOIN taxa ON (taxa.taxid = pairs.thermo_taxid)WHERE pairs.query_align_cov > 0.95AND pairs.subject_align_cov > 0.95AND taxa.temperature > 80.070.This returns protein pairs in <10 seconds. See the README in the zipped data file for some additional example queries. DuckDB allows for exporting to csv, parquet, and many other desired formats, thus a user can retrieve the specific data they require on a taxa, protein, or protein pair basisWe have also provided dumps of the database tables as semicolon separated value files, however they may be unwieldy and it is recommended to use the database interface.et al.63 via UniProt ID. Comparing the labels for the records in both datasets (N = 1.6 mil) yields an R2 of 0.995. Some small differences in OGT labels arise due to our strain aggregation procedure, described above.To ensure a proper mapping of OGT label to protein, we have joined our proteins with enzymes from Engqvist M labels49. A Spearman’s correlation of 0.85 with a p-value of 0.0 was observed between these two quantities, suggesting a strong correlation. Furthermore, a binomial test was performed to determine if the melting temperature has a >99% chance of being greater than OGT, yielding a P-value of 2.68e-19. Figure To validate that the optimal growth temperature (OGT) is a suitable substitute for melting temperature as a measure of thermostability, and to ensure that temperature data has been accurately mapped, we compared proteins within our dataset to existing melting temperature datasets. We paired wild type proteins from FireProtDB and the Meltome atlas to proteins within our dataset using >99% coverage and identity, yielding 4,640 proteins with both internal OGT labels and external Tet al. previously produced the current largest dataset of functional protein pairs across temperature at 1.6k pairs by starting with structures of PDB entries52. This dataset served as our benchmark, with the quality of their protein pairs used for comparison. To facilitate this comparison, we first aligned their dataset using DIAMOND, and subsequently compared the resulting alignment metrics to ours69. Statistically similar or even superior alignment scores to the baseline were observed in our dataset, as depicted in Table Hait 73. Retaining matches with an E-value < 1e-10  resulted in 86.1% of our proteins and 99.8% of Hait’s proteins being labelled with at least one annotation. This suggests that our data includes novel proteins not extensively represented in Pfam. We evaluated the quality of a protein pair according to Pfam by calculating the set Jaccard score of accession annotations  and 100%. The cumulative distribution of probability scores from FATCAT for each of these subsets is given in Fig. We used FATCAT to align PDB structure of the baseline pairs with flexible alignment82. To facilitate this test, we preprocess our data using the following steps: binarization by OGT <30 °C or > = 60 °C, class balancing, deduplication of similar sequences, and splitting based on NCBI taxonomy of host species, resulting in a training and test set of 290k and 28k proteins respectively, each labelled as mesophilic or thermophilic 83. The TemStaPro ensemble outputs predictions in bins of 5 °C between ‘<40’ and ‘65< = ’, and conducts a self consistency check between ensembles. On our data, the model is consistent for 95.5% of examples, and of those, predicts the correct class with 91% accuracy compared to the null model of predicting the majority class with 61% accuracy. The distribution of predictions is shown in Fig. We then evaluated a recent predictor, TemStaPro, on our test set of 28k proteins83. The training parameters and architectural details can be found in Supplementary Information Given that TemStaPro was trained on UniParc proteins, it is possible that the model has seen data from our test set before. To ensure that high performance is not due to data leakage, we trained our own model on the development set of our data. We finetuned ProteinBERT, a LPLM, on our development setIt is clear that the data we produced is capable of supporting the foundational machine learning task of thermophilic classification, yet with almost an order of magnitude more data than previously. With the addition of homologous pairing, as well as its increased size, the dataset will open the door for more complex models such as thermal stability design models.https://dvc.org). A user can reproduce this data with a single command ‘dvc exp run’ (assuming available compute resources) or run the data pipeline using different parameters and produce a variant of the dataset, all the while monitoring how the data changes. Environments and parameters necessary for this process are retained within the repositories.The codebases that produced the results in this work are free and openly available. The pipeline leverages Data Version Control (DVC, A snapshot of the data repositories at the time of production of these results is provided as a Figshare dump in addition to the repository links. Each of these repositories is DVC tracked, thus the set of parameters used to execute the pipeline is found in ‘params.yaml’. A description of each parameter is given as comments in the file, and in Supplementary Information Supplementary Information


In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research. Protein molecular design refers to the comprehensive use of multidisciplinary techniques to obtain novel proteins with better target performance than natural proteins based on the structure–function relationship of proteins. This process mainly involves establishing a structural model of the target protein, studying the structure–function relationship, proposing a reasonable design and renovation plan, and further modifying the design through experimental verification, which often requires multiple iterations to achieve the desired purpose  1]. The. The1]. Early work in protein design focused on redesigning helical bundles , using sIn recent years, the updated optimization of artificial intelligence algorithms, increasing arithmetic power of computer hardware, and massive expansion of experimental protein structure data have created favorable infrastructure for intelligent protein design, resulting in many remarkable results . In 2019The core of intelligent protein design involves establishing a relationship between structure and function. Therefore, the prediction of protein structure and function by artificial intelligence algorithms is also a key aspect of protein design, apart from in the above-mentioned intelligent protein design methods that directly modify protein structure to target the desired performance. AlphaFold2 , which hIt is evident that the latest advances in artificial intelligence algorithms  can boost the overall intelligent protein design process by assisting protein structure modification and structure and function prediction ,13,42,43Artificial intelligence has been used in many applications in the field of protein engineering; including protein structure, function, thermal stability ,46,47, aProtein structure prediction is a critical step in the intelligent design of proteins and is a fundamental scientific problem in the field of protein computation. This problem can be traced back to the famous statement made by Christian B. Anfinsen (the Nobel laureate in chemistry in 1972), that the AA sequence of a polypeptide chain contains all the information about its 3D structure . CurrentTo address this problem, the academic community has been organizing the critical assessment of protein structure prediction (CASP) competitions since 1994, which has greatly promoted the development of computational methods for protein structure prediction. For example, I-TASSER  represenThe advent of AlphaFold2 and RoseTTAFold has increased the accuracy of protein structure prediction to a new level. However, these methods are not effective at predicting the structure of orphan proteins because of the lack of homologous proteins. In October 2022, Chowdhury et al.  proposedIn addition to protein monomer structure prediction, multimer structure prediction has also been studied recently. In October 2021, the DeepMind team developed AlphaFold-Multimer , with inThe primary sequence of a protein determines its high-level structure that determines its function according to the golden rule of sequence–structure–function correspondence. Thus, the protein sequence ultimately determines protein function. A deep understanding of the relationship between protein sequence and function enables the rapid localization of novel protein functions that facilitates de novo protein design by direct sequence modification. The advent of low-cost and efficient sequencing technologies has driven rapid growth in the number of protein sequences ,77. The In 2020, Hippe et al.  proposedVarious types of information were used for automated prediction of protein function in addition to sequence-based methods for intelligent prediction, such as domain-based prediction ,83,84, hThe emergence of de novo protein design can be traced back to the 1980s, when DeGrad et al.  made a pThe automatic design method based on energy functions is not limited by the type of main chain structure compared with the purely heuristic design method. Furthermore, the specific spatial accumulation between residues and the quantitative calculation of hydrogen bond interactions improves the success rate of the design. In the 21st century, Baker first designed protein folding that does not exist in nature, leading to the de novo design of protein backbones. In 2008, Baker proposed an inside-out protein design strategy to artificially create several non-natural enzymes  to catalyze the polymerization of formaldehyde using a specific natural benzaldehyde lyase (BAL) unearthed from a database and employing Foldit and RosettaDesign tools . Further2 . Most ofData-driven approaches to de novo protein design  have also emerged in recent years ,40,41,95De novo protein design using computational design has entered an unprecedented era, wherein the structural and functional design of increasingly complex proteins would be possible with the continuous iterative optimization of energy functions, main chain design, and side chain optimization. A recent review by Ovchinnikov and Huang described how structural information can replace traditional backbone design, side-chain optimization, and energy functions . Huang uMolecular characterization refers to measuring molecular properties in a certain aspect that is either the basic physical and chemical properties of molecules, or numerical indicators or vectors derived from the molecular structure using various algorithms to describe the structural information of different layers of molecules . They caIn the early years, computer development was relatively delayed and hardware standards were low. Traditional classical descriptors were widely used for crude characterization of protein macromolecules, owing to their simplicity, ease of understanding, and low arithmetic requirements. These traditional descriptors typically quantitatively describe the intrinsic properties of a macromolecule based on its molecular composition and physicochemical properties, including the frequency of AA occurrences in the protein composition , the isoEarly traditional descriptors in intelligent protein design were mostly used in studies of protein–macromolecule interactions, protein–small molecule interactions, and protein functional site predictions ,148,149.Protein sequence determines the three-dimensional structure. Therefore, the protein sequence contains advanced structural information. Most protein-related studies have employed sequence information to characterize the proteins when protein structures were difficult to resolve and computing power was insufficient. One-hot and K-mer characterization methods were used extensively, owing to their simplicity and ease of understanding, low computational effort, and high efficiency ,153,154.A large variety of NLP models have emerged with a profound impact on the study of intelligent protein design following advances in sequencing technology, the development of deep learning algorithms, and significant improvements in computing power. In 2017, Google released transformers based on the attention model that started a new era of NLP . This grAdvances in the transformer era inspired several studies to apply the concept of language models to protein design. In February 2019, Yu et al.  applied NLP-based learning models for protein sequence representation have achieved remarkable results in protein design ,178,179.It is anticipated that NLP models will be further improved by simplification and reducing their dependence on computing devices. New protein characterization methods will be developed to better represent the relationship between protein and natural language. Alternatively, we may see the continued growth of protein sequences and the implementation of quantum computers that will allow protein design models to achieve human-like thinking and precisely achieve the second law proposed by Manfred Reetz: “You get what you designed” .Proteins are composed of one or more peptide chains, and the connections and folding patterns of each peptide chain constitute their special three-dimensional spatial structure . The uniTopology-based protein characterizations describe AAs based on the atomic linkage indices generated from molecular graphs. These mainly include traditional T-scale and ST-scale topology descriptors, and newer meta-graph and circuit topology descriptors.In 2007, T-scale was proposed by Tian et al.  based onA meta-graph is a newly proposed graph structure that differs from the traditional network themes or sub-graphs. It captures specific topological arrangements involving interactions and associations between proteins and keywords. Each protein can be described by a series of meta-graphs illustrating its interactions with other proteins and their associations with keywords. Proteins with similar functions often exhibit similar meta-graph representations .Circuit topology is a newly proposed descriptor that theoretically assesses the relationship between contact pairs on the protein backbone and provides information about the protein structure (such as the order of residues and residue contacts). The use of circuit topology to predict the folding rate of proteins has improved pathogenicity prediction of missense mutations .n × n matrix, and descriptor values can then be obtained using matrix decomposition or image processing techniques. The contact graph is a binary graph obtained by setting a distance threshold on the distance graph. Distance and contact graphs have the advantage of rotational or translational invariance of the protein structure and low dimensionality, which makes them computationally efficient. Currently, contact, and distance graphs were extensively used in protein structure prediction methods, such as AlphaFold [Protein distance graphs can be obtained by calculating the distance between Cα atoms or neighboring residues. A protein of length n can be represented as a lphaFold , trRosetlphaFold , C-I-TASlphaFold , C-QUARKlphaFold , DeepFollphaFold , and so Geometry-based protein characterization is related to indicators representing the structural features of a protein, such as the locations of atoms in space, and the shape and size of the protein. These include point clouds , three-dA point cloud is a set of points representing object-space partitioning and external attributes in the same spatial reference system. It is a group of isolated nodes with a given position in 3D space, called a 3D point cloud . Point cThree-dimensional tessellations allow graphical representation of proteins by dividing the three-dimensional space into cells with specific properties. Each node represents a cell and any contact between two cells is represented by each edge. A Voronoi diagram is a typical type of tessellation that describes the structure and interactions of proteins and is mostly applied in structural bioinformatics . For exaThe 3DCNN divides 3D space into multiple grids, allowing direct manipulation of atomic positions in space by vowelizing the structure and facilitating the capture of the local microenvironment of the protein structure. Thus, the 3DCNN automatically extracts protein structural features and has powerful structural characterization capabilities that are compatible with the detection of structural patterns, binding pockets, and other important structural features of specific shapes. Li et al.  used deeThe GVP-GNN introduces Geometric Vector Perceptrons (GVPs) and extends the standard dense layer to enable manipulation of a collection of Euclidean vectors . By intrProtein design often relies on three-dimensional structural data to fully capture the functional information of proteins. It is typically richer than the information provided by sequence data. However, current models predominantly use sequence features, owing to the lack of proper 3D structure characterization methods. Most are computationally expensive and cannot avoid information loss when dimensional reduction is performed. Furthermore, deep learning models may not fully explore the hidden information in high-dimensional data . ConsequAt present, intelligent protein design is in a boom period, and several intelligent protein design models were developed, including SCUBA, ABACUS, ProteinMPNN, and RFdiffusion. This significantly improved the success rate and computational efficiency of protein design. However, the accurate and rapid protein design concept of ‘You get what you designed’ is yet to be realized in practice.Effective protein characterization is essential for intelligent protein design. Four protein characterization methods , were introduced. Traditional protein representation methods were applied in the early days due to their simplicity and ease of understanding. However, they could not comprehensively represent proteins. The similarity between natural language and protein sequences resulted in sequence-based protein characterization methods based on NLP becoming the main method for protein sequence characterization. Structure-based protein characterization methods, such as point clouds based on spatial coordinates and GVP-GNNs based on geometric vectors, have also received widespread attention with the rapid development of protein structure prediction methods and artificial intelligence algorithms. However, their applications are limited because of their high computational requirements. Researchers have attempted to integrate sequence and structural information to represent proteins to comprehensively consider computational power and protein characterization; however, determining the best combination of multiple features remains still an open question.Although the representation of proteins for intelligent model construction is largely resolved, there is no consensus on which representation is most appropriate for characterizing proteins. We believe that a large amount of protein structure resolution and the development of intelligent algorithms will inspire new efforts to improve protein characterization. This would promise to accurately extract useful information from the vast amount of data, and associate sequence structure information with functional phenotypes to enable efficient and accurate protein design with new functions.