Emerging computational tools promise to revolutionizeprotein engineeringfor biocatalytic applications and accelerate the development timelinespreviously needed to optimize an enzyme to its more efficient variant.For over a decade, the benefits of predictive algorithms have helpedscientists and engineers navigate the complexity of functional proteinsequence space. More recently, spurred by dramatic advances in underlyingcomputational tools, the promise of faster, cheaper, and more accurateenzyme identification, characterization, and engineering has catapultedterms such as artificial intelligence and machine learning to themust-have vocabulary in the field. This Perspective aims to showcasethe current status of applications in pharmaceutical industry andalso to discuss and celebrate the innovative approaches in proteinscience by highlighting their potential in selected recent developmentsand offering thoughts on future opportunities for biocatalysis. Italso critically assesses the technology’s limitations, unansweredquestions, and unmet challenges. With the introduction of directed laboratoryevolution came the opportunity to tailor native enzymes to desiredprocess conditions, accessing and improving activity for novel andnative substrates, increasing stereoselectivity, and adapting to reactionenvironments of choice. Compared to attempts of rational enzyme engineering,Darwinian evolution proved more effective in successfully navigatingthe immense size and complexity of protein sequence space. In parallel,these efforts were greatly aided by advances in DNA sequencing, high-throughputscreening, and analytical methods. Nevertheless, the gradual climbup “Mount Improbable” is time- and labor-intensive andcomes with the risk of being deceived by false summits. Offsettingsome of these risks, computational tools have quickly become invaluableguides in design and analysis of experimental directed evolution workflows, as well as fine and bulk chemicals.in silico methods representan essentialcomponent of the design–build–test–learn cyclein enzyme engineering. Promising a faster, cheaper path to high-performancebiocatalysts, the combination of bioinformatics and machine learningalgorithms offers new tools to potentially locate advanced enzymeengineering starting points and more direct routes to de novo and highly engineered high-performance biocatalysts. Multiple excellentreviews and perspectives already summarize applications of this rapidlyexpanding field.6Today, Machine learning comes with the great promise to accelerateenzymeengineering, similarly to the potential that machine learning (ML)and artificial intelligence (AI) have in life sciences.In everydayconversations and reports, the terms AI and ML areoften used interchangeably. These concepts are related, but distinct.AI is the broader concept of machines that are able to perform tasksin ways that we would commonly associate with human-like intelligence.The list of such tasks includes items from categories like problemsolving, decision making, and, above all, learning. ML, on the otherhand, is a set of computational tools from various paradigms thatessentially uses algorithms to describe, analyze, and learn from data.ML algorithms can identify patterns and relationships among enzymes,sequences, active sites, or other aspects of relevant data; make predictions;and help to take actions in order to achieve a specific academic orindustrial goal. Noteworthy applications of ML in life sciences aredrug discovery, the development of new medical treatments, enzymedesign and synthesis, and targeting specific enzymatic propertiesand abilities.In terms of concrete machine-learning methodsthat have been successfullyused for the purposes described in this perspective, it is straightforwardto note that model uses and architectures have evolved more or lessanalogously to the typical evolution of the most successful modelsand architectures in any application of machine learning. Startingfrom decision trees and support-vector machines around the turn ofthe century, deep learning took over shortly after 2010, and subsequentdevelopments have followed the typical path from simple deep-net architecturesvia convolutional neural networks to recurrent neural networks, graphneural networks, and more complicated architectures. In this Perspective, westart with an overview of recent examplesthat successfully have applied AI and ML for tailoring biocatalysis,focusing specifically on the pharmaceutical industry. Next, we pivotto explaining current and emerging methods in greater depth. Giventhe still narrow penetration of ML approaches into this field, weexplain the fundamentals and showcase the potential of various methodsand approaches in enzyme engineering, enzyme discovery, and design.Throughout the manuscript, we discuss a number of relevant subtopics,present instructive examples, and provide our perspective on the implicationsof the use of ML methods for each problem or application. Finally,we attempt to look ahead to the future of the field and make somerecommendations.kcat/Km. Becauseindustrial processes aim to operate at high substrate loadings, preferablywithout product and substrate inhibition, Km often becomes irrelevant and more decisive parameters are kcat and operational stability. As a rule ofthumb, it is been proposed that kcat >1 s−1 isdesirable for an industrially relevant biocatalyst. Therefore, classicalenzyme engineering for use in industrial processes aims to intensifyvariant-screening conditions each round to bias the evolution towardprocess relevance. In machine-guided evolution, however, changingreaction conditions at each round need to be taken into account inthe modeling and training processes in order to reliably account forany effects in the data that stem from changes in the setup insteadof other variables. As a further challenge, enzyme engineering forindustrial applications usually seeks to improve multiple propertiessimultaneously. Typically, high activity and selectivity are performanceindicators of choice, often combined with elevated thermostabilityand tolerance of nonaqueous solvents.In enzyme engineering for industrial biocatalytic applications,the desired end points are often slightly different from classicalefficiency measures of an enzymatic reaction, often expressed as 1 toward an API candidate ZPL389 2  catalyzed asymmetric reductive amination ofketone ZPL389 2 1.14 Theivolution 2, startiThe work aimed to optimize both conversion andenantioselectivityof the reductive amination. Since conversion was determined at a singletime point (overnight reaction), it could be argued to reflect bothactivity and stability components.Escherichia coli in small volumes inevitablyincreases the variability of data. To help account for this variability,multiple measurement points for each variant were taken and the trueproperty values were then estimated using Bayesian estimation.16 In conclusion, the machine-directed approachyielded variants on par with traditional directed evolution whileproducing much fewer inactive variants. Reducing the experimentalnoise in the training data might have further improved the variantsgenerated by the machine-directed approach. For applicability in shortproject timelines, a “low-N” based methodology17 or similar would be highly advantageous andwould allow for higher precision of input data. Cases where individualvariants could be expressed on a larger scale and even purified foractivity and selectivity measurements would significantly increasethe quality of data fed into the model. However, the practicalityof such an approach strongly depends on the number of variants neededto train a model that is useful for choosing the next round of variantsselected for testing.The IRED evolution approachrelied on a 384-well plate format becausethe required automation was readily accessible and the team envisioneda relatively large data set and thus high throughput to enable initial(machine) learning. However, homogeneous culturing and enzyme expressionfrom In the following literature examples,we aim to highlight ML-assistedenzyme engineering work toward biocatalytic applications with relevanceto pharmaceuticals. Currently, the studies still focus on methodology,and complete enzyme engineering campaigns toward API manufacturingare yet to emerge.18 and Liao et al.19 augmented traditional directed laboratory-based evolution with MLstrategies. While Fox et al. introduced statistical analysis of proteinsequence–activity relationships (ProSAR) to raise the volumetricproductivity of a bacterial halohydrin dehalogenase by ∼4000-fold,Liao et al. were able to improve the activity and heat stability ofproteinase K toward the hydrolysis of a tetrapeptide by roughly 20-foldusing only 95 variants. In both examples, computational analysis ofkey performance features in initial variants guided the design ofsubsequent rounds, thereby reducing the number of tested variantswhile achieving rapid functional gains, highlighting one of the mainarguments for leveraging computational tools to increase the efficiencyof protein engineering.As early as 2007, Fox et al.15 Encouragingly, both training sets yielded predictions with comparablyimproved activities, which were 2.2–2.5× higher than thatof the known positive variant.Sortase, an enzyme applied in enzymaticbioconjugation , was a 21 islatravir,22 and MK-1454,23 their quest to accelerate the evolution processesincluded studies of a broad spectrum of sequence- and structure-basedmethods  for mutation predictionsin collaboration with ATUM.24 The teamstudied ATA-117, which is a widely applied (R)-selectivetransaminase in the industry. For an initial round of a transaminaseevolution, these small (<100 variants) predicted libraries contained9% and 18% improved variants over the starting point. Combining thesemutations resulted in a relatively high number of inactive variants. Nevertheless, several variants with 7–9×wild-type activity were also identified.At Merck & Co, Inc., scientistsand engineers were early adoptersof biocatalysts and champions of enzyme evolution for API manufacturing.Beyond seminal engineering work with Codexis, including biocatalyticprocesses in the manufacture of sitagliptin,25 Wild-type halogenase WelO5* did not catalyze chlorination or hydroxylationof the target substrate soraphen A  with a 13-fold increase in total halogenationactivity was identified. For the remaining sequence landscape of thelibrary, a Gaussian process model was used to predict higher-activityvariants. Four of the seven predicted variants outperformed the firstidentified variant with up to 16-fold total halogenation activity.Kinetic characterization toward one of the chlorinated soraphen Aanalogs (3a) revealed that although the experimentallydiscovered variant V81S/A88L/I161P exhibited higher kcat (2.41/min) than a predicted variant A88L/I161A (kcat 1.96/min), the total turnover number (TTN+)was higher for the latter (92 vs 30), leading to higher product titerswith the predicted variant as opposed to the experimental variant.The Gaussian process model prediction clearly reduced the screeningeffort for the 8000 variant library, which for a 95% coverage withNNK saturation would have required the analysis of ∼100 000variants.26More recently, Büchleret al. reported an interesting studyfor engineering a halogenase, which is very relevant to late-stagefunctionalization in pharmaceutical research.hen A 3, 1; howeve27 and some years later an approach named innov’SAR28 to improve the enantioselectivity of the kineticresolution of rac-4 (ΔΔG‡ in the study) of all 29 = 512 possiblecombinations of variants. It was very interesting to see that epistaticand additive effects were predictable using only single-variant trainingdata. The authors observed that Fourier transformation, which innov’SARapplies to generate protein spectra from the numerical encoding ofa sequence, improved the model’s prediction performance. Afterincorporating 28 variants with multiple mutations into training themodel, the enantioselectivity  of five arbitrarilyselected predicted variants was experimentally measured. Measuredvalues correlated well with the prediction . Furthermore, two predicted and measured mutantsyielded in much higher E-values (>200) towardtheresolution of rac-4 than the originalbest variant from the experimental campaign (E =115).Engineering the enantioselectivityof enzymes has been a research interest of the Reetz group for a longtime. In the earliest adaptation of machine-directed evolution towardimproved stereoselectivity of a biocatalyst, an epoxide hydrolase,they applied an adaptive substituent reordering algorithm (ASRA)of rac-4 1. In the29 This non-natural reaction was catalyzed (S)-selectively (ee (S)-8 = 76%) by theY32K/V97L variant of a putative nitric oxide dioxygenase from Rhodothermus marinus (Rma NOD), which wasused as a parent enzyme for evolution. During the machine learning-assistedevolution, two sets of amino acid positions were explored. First,four amino acid positions were targeted and combinatorial librarieswere built using NDT codons for diverse properties of 12 amino acidsat each position. 124 randomly sampled variants from this librarywere then used as a training set, and subsequently 90 predicted variantsfor both (S)- and (R)-selectivitywere tested. This first prediction round resulted in 86% ee for (S)-8 (variant VCHV) and 62% ee for (R)-8 (variant GSSG). Those most selective variantswere then used as parent sequences for the next round of machine learning-assistedengineering at three new amino acid positions. Variants providingenantiomeric excesses of 93% and 79% of the (S)-and (R)- enantiomers, respectively, were identifiedas a result  tool was integrated intothe web server EnzymeMiner33 for automatedmining of novel soluble enzymes from protein databases.Separately,SoluProt is a computational method for the sequence-based predictionof solubility and soluble protein expression in 34 Whilethis method is able to “learn” from natural protein-sequencediversity and enables the generation of functional protein sequences,the algorithm also yielded a high proportion of soluble enzymes. Ofthe experimentally tested sequences, 35% were found to express solublyin E. coli, out of which 68% retained measurablemalate dehydrogenase activity .Finally,Repecka et al. trained a generative adversarial network(GAN) model on a wild-type malate dehydrogenase in order to identifyfunctionally improved variants.35 For more examples and scientificdetails, we refer readers to a recent review presenting a perspectiveon the computational design of stable and soluble biocatalysts.30In addition tosoluble protein expression, enzyme stability duringengineering campaigns and under process conditions is a key factorto consider. Multiple studies have shown that engineering enzymestoward higher thermostability also increases their tolerance towardorganic solvents; thus, the mechanisms for improving stability somewhatoverlap.36 as the optimum pH 5.5 for thewild-type enzyme is not well compatible with applications such asglucose monitoring from physiological samples. For direct biocatalyticapplications, it is worth noting that glucose oxidase can be appliedas an in situ hydrogen peroxide generator when thisdrives further biocatalytic reactions.Simultaneous and efficientoptimization of multiple parameters is the ultimate goal toward industrialbiocatalysis. Typical features include activity, selectivity, andstability, as well as the alleviation of inhibition. Glucose oxidasewas engineered toward higher mediator specificity across broader pHrange,37 While we can expect a general methodical approach to remainvalid for other use cases as well, one may also ask how much of thedetails in terms of a particular methodology and setup used in themultiparameter optimization of glucose oxidase, for example, wouldimmediately work for galactose oxidases and ultimately for alcoholoxidases.A question encounteredin this context of AI-driven enzyme evolution is how well one particularmethod’s specific applicability will hold up across differentenzyme classes. For example, galactose oxidase has at times servedas a scaffold to stereoselective alcohol oxidases.38 Importantly, the generated model could predictvariant activity on a different transaminase backbone after retrainingon a small additional data set. A methodical approach was taken consistingof (1) the rational design of a mutant library of transaminases (PDB 3FCR) with diverse activitiesand stereoselectivities; (2) the collection of a standardized, high-qualitydata set matrixed from 32 variants, the wild-type 3FCR, and 13 pairs ofenantiopure substrates; (3) building a predictor for catalytic activityand stereoselectivity; and (4) the prediction and validation of catalyticproperties of new variants. After model validation, the authors thenlooked into extending the prediction to another transaminase, 3HMU, which shares similaractive-site architecture to 3FCR. However, updating the predictor with some 3HMU-relevant featureswas necessary to reach acceptable correlation between predicted andmeasured values on 3HMU.A step toward generalization of models was recentlydemonstratedin a transaminase study addressing both activity and stereoselectivity.39 with various modes of action. Many therapeuticenzymes have been already approved by the FDA and brought to market.40 In contrast to small molecules, the use of MLtools for biotherapeutics is still in its early stages. This is probablydue to the nature of the enzyme’s application, as additionalproperties such as safety, formulation and physical and chemical stabilityneed to be addressed in parallel and so far only smaller data setsare available. In this context, ML has a great potential to impacton the optimization of protein properties to accelerate the developmenttimelines and manufacturing cost of biotherapeutics.41Biocatalysis has made animpact in the synthesis of chemicals and pharmaceuticals over thelast years. Beyond catalysis in the world of low-molecular-weightcompounds, there is also the opportunity to use enzymes as therapeuticsenzyme discovery in orderto distinguish it from the closely related terms enzyme or protein engineering as well as optimization. Forthe purpose of this Perspective and the sake of clarity, we wouldlike to define enzyme discovery as a process in whicheither a new protein is discovered that exhibits a new or known enzymaticfunction or activity or a known protein is shown to possess an enzymaticfunction or activity that was known from other enzymes, but not forthis particular protein. A particular aspect of the latter case isenzyme promiscuity,44 where the new activity appears in addition to theenzyme’s main function.First, we would like to definewhat we mean by the term In contrast, as soon as any particularprotein has been known to be active with regard to a particular enzymaticreaction, further improvement, modification, or mutational analysisof that reaction would thus have to be considered enzyme optimizationor engineering instead.We would like to note here that enzymediscovery is not predictableper se. Despite this marked difference to the processes of engineeringand optimization, there are various mechanisms in our arsenal thatallow for enzyme discoveries being made while being aimed at somethingelse, be it to some or to a major extent.46 Directed evolution per se is intended to improve a given markerfor a protein by mutating and selecting from the “fittest”resulting mutants and is mostly intended for protein engineering.However, enzyme discovery happens during these processes and can befocused on by allowing for new functionality and using further directedevolution to enhance the newfound function.47One example of sucha mechanism is the discovery of new enzymatic functions employinga setup that uses directed evolution in order to optimize a completelydifferent enzymatic function of a set of proteins and their variants.48 There, the actual functional analysis may involve challenges, e.g.,expression differences in genes found in the overall sequencing effortwhen they are cloned to other hosts.49 Onthe other hand, recent efforts have led to a growing toolbox, whichhas ample room for the application of machine learning as well asstandard techniques.50Another example is the appearance of new enzymatic functionalityin the analysis of metagenomic studies.51 wherethe known involvement of a particular protein in causing the diseaseis suddenly explained by discovering the underlying enzymatic activity.This kind of discovery may appear arbitrary at first, but is madevery plausible if not likely in the light of the extensive networkof human enzymes and the way their activity is interlinked acrossmultiple different metabolic pathways and diseases.52A furtherexample is the appearance of a new enzyme function inthe study of disease mechanisms,In summary, enzyme discovery is overshadowed by enzymeengineeringin the literature and certainly in practice but must not be neglecteddue to its potential for exciting new functionality that is waitingto be discovered.53In thecontext of this Perspective, we have to emphasize the various waysin which bioinformatics methods can contribute to enzyme discovery.We already mentioned enzyme discovery as a possible byproduct of adirected evolution effort. While directed evolution is not bioinformaticsas such, the computational efforts to aid in this successful methodare increasing in amount and sophistication.54 For such surface-based interactions,it is natural to also look for smaller molecules that could interactsimilarly with the protein surface and lay the groundwork for an enzymaticreaction.That is not all, however. There are several processes thatrepresent very active fields of research in bioinformatics and atthe same time could have a drastic impact on enzyme discovery. Onesuch example is protein–protein interaction, where one wouldlike to predict a range of effects that two proteins can have on eachother.55 There, one might find a new enzymatic functionalityduring the experimental validation phase of the corresponding searchprocess. In both these kinds of setups, bioinformatics methods havebeen very successful in recent years, and machine learning is becomingincreasingly important in these computational approaches.56An analogous result can emerge from the studies ofsmall molecules that bind to the active sites of proteins.57 Itis the specialization and optimized nature of enzymatic activity assuch that makes this kind of relational insight possible.In terms of concrete methods, we wouldlike to stress the importanceof machine learning in an open discovery process. In this context,some caveats apply, since it is not straightforward to pinpoint anyparticular direction for the next scientific discovery. Still, usefulstrategies follow via an evolutionary picture of phylogenetic andother connections among proteins.A key factor in the successof applying ML to protein engineering is the choice of representationof the protein. Protein representations are essential for ML modelsto process the complex information encoded in protein sequences andstructures. Similar to chemical fingerprints used in cheminformatics,protein fingerprints are employed to represent proteins.58Somecommon protein representation methods include one-hot encoding, whichis a binary representation of protein sequences where each amino acidis represented by a unique binary vector. In other words, the proteinsequence is converted into a matrix where the rows represent aminoacids and the columns represent the position in the sequence, eachcolumn having a 1 at the row representing the amino acid at that positionand a 0 otherwise. Property-based representations capture specificphysicochemical properties of amino acids, such as hydrophobicity,charge, and size. These representations are mostly residue-based,focusing on individual amino acids and their properties.59 This approach allows for theconsideration of the broader context of the protein sequence, accountingfor the relationships between amino acids and their evolutionary history.Evolutionary-based methods for representingproteins, on the otherhand, work on full sequences and incorporate information from homologoussequences.60 autoencoders,61 and other deep-learning models, can learn moreabstract and meaningful representations of protein sequences and structures.These models are trained on large amounts of protein sequence and/orstructural data to capture complex patterns and relationships withinthe data, resulting in more information-rich representations. In contrast to the so-called fixed representations mentioned above,learned representations, which are generated by advanced methods likelarge language models (LLMs),The main difference between learned representationsand fixed representationslies in the fact that learned representations can learn intricate,high-dimensional relationships within protein sequences. Predictivemodels that are based on such learned representations can potentiallyextrapolate and make more accurate predictions even in unexploredareas of the sequence space, meaning they can predict the effectsof mutations that are not explicitly present in the training data.This feat is hardly achievable using fixed representations. Thus,using learned representations can enhance model performance in variousprotein engineering tasks, such as protein design, function prediction,and protein–protein interaction prediction.60 The resulting protein languagemodels are capable of producing meaningful representations of proteinsequences62 that could be used for downstreamstructure prediction or variant-effect-prediction tasks without needingto finetune the models on specific enzyme families or folds.63A commonstrategy to get sequence-based learned representationsis to train LLMs on protein sequence databases to learn the “grammar”of evolutionary and structural information encapsulated in proteinsequences.3D protein structures can similarly beturned into both fixed andlearned representations. While fixed structure representations, similarto fixed sequence representations, rely on predefined known propertiesof the protein structures, such as 3D atomic coordinates, learnedstructure representations often use deep learning models like graphneural networks (GNNs), which can learn to represent the protein structurein a way that is most useful for the specific task at hand.64 uses a relational graph convolutional networkto model the interactions between residues in a protein. It also introducesa new concept of edge message passing (as opposed to message passingbetween nodes of the protein graph) to model different spatial interactionsamong residues. For more background on message passing neural networks,see Gilmer et al.65 In a further step,the group then combined a protein LLM (ESM-1b) with their GearNetmodel to generate structure-aware protein sequence embeddings.66The GearNet model published recently by Zhang et al.67 investigated howto create meaningful protein representations for various tasks, suchas enzyme engineering. They found that the optimal choice of representationdepends on the specific task and that different biological aspectsof a protein will place different demands on the representations.For instance, some tasks may require a representation that capturesthe global properties of a protein, while others may need a more detailedview of local properties. This implies that a single protein representationmay not be suitable for all tasks and that it is essential to considerthe specific requirements of each task when designing a representation.Detlefsen et al.Interestingly, the authors also discovered that fine-tuning ofLLMs can sometimes lead to overfitting and reduced performance. Fine-tuningis often used for models producing learned representations, whereadditional protein sequences closely related to the engineering targetare shown to the model, thereby changing the overall representationsproduced by the model. This highlights the importance of carefullyconsidering the trade-offs between fine-tuning and using a non-fine-tunedrepresentation when working with protein data.i.e., thatwe are able to predict an experimentally measurable property fromeither the sequence or the structure of a protein.Having chosen a suitableprotein representation, ML models can be trained to predict proteinproperties based on the representations, or even generate completelynovel protein variants with desired properties 4. When d68 In Ma et al.,14 random forest models were trained to predictthe activity and stereoselectivity of variants of an imine reductase.The models were then used to prioritize variants to test experimentallyfrom a large set of novel in silico generated variants.The selected variants achieved similar fitness to variants found viaclassical brute-force-directed evolution. For such straightforwardprioritization tasks, non-deep-learning models such as the randomforest model can give satisfying results. However, the authors alsosaw that the model was not able to accurately predict the fitnessof out-of-distribution mutants, meaning enzyme variants with combinationsof mutations that had not been observed in the training data.Scikit-Learn offers a range of basic ML modelsthat can be usedfor this purpose, such as random forests, boosting trees, and supportvector machines (SVMs).69 initially designed for image-prediction tasks,have emerged as a consistently well-performing deep learning toolin protein sequence–function prediction tasks.To have better predictive power in such unseen parts of sequencespace, or in general to increase model power, deep-learning modelscan offer a solution. Convolutional neural networks (CNNs),70 conducted an extensive studyon the performance of various ML models as well as the underlyingprotein representations in accurately predicting protein propertiesin protein engineering campaigns. After evaluating predictive performanceof different deep-learning as well as non-deep-learning models onpublic and proprietary data sets, they found that CNN models, whentrained on amino acid property descriptors, were on average the best-performingmodels.Xu etal.71 added CNNs to their suite of models used ina paper exploringmachine learning-guided directed evolution (MLDE). They again evaluateddifferent models and protein representations on their effectivenessin guiding the exploration of the empirically determined fitness landscapeof the GB1 protein. Given that sufficient training data points weremade available to the models (in this case 384 variants), CNNs consistentlyranked among the top-performing models in predicting the fitness ofunseen variants.Encouraged by these results, Wittmann et al.Another interesting point to mention here isthat the authors foundthe composition of the initial training data to matter a lot in termsof how well the models aided in finding optimal variants. Inclusionof too many unfit variants in the training data hampered model performance.Since in reality most mutational variants of proteins are nonfunctional,and often the fitness landscape of the protein target is not knownin advance, the authors propose the so-called zero-shot predictionof the fitness of initial variants. Zero-shot prediction models arecapable of predicting protein fitness without any labeled trainingdata specific to the problem. Among the tested methods, EVmutation,mask filling using the evolutionary scale modeling (ESM) protein languagemodel, and triad DDG calculations, were effective in identifying fitGB1 protein variants, with the mask-filling protocol using the ESMmodel being a notable exception to the general failure of mask filling(which is used to train most current LLM protein models) as a zero-shotpredictor.72 furthersubstantiatedthe efficacy of CNNs in protein design coupled with Bayesian optimization.They compared three surrogate models (probabilistic predictors ofprotein function) for protein engineering on synthetic benchmarksand found that CNN ensembles trained directly on primary sequencesoutperformed Gaussian processes and models built on pretrained learnedembeddings. The superior performance of CNNs was attributed to theirimproved robustness on out-of-distribution data, a critical aspectin protein engineering where the search spaces are often vast andcomplex.Gruver et al.In conclusion, these studies collectively underscorethe potentialof CNNs as an effective tool for protein and enzyme engineering.73 generative adversarial networks(GANs),74 and transformer-based models,75 among others, can be used. A prominent examplein the chemical space is the use of VAEs to generate new moleculeswith desired properties.76For the generationof new variants based on sequence information, variational autoencoders(VAEs),77 used a VAE trained on 70 000 luciferase-like oxidoreductasesto generate novel functional variants of the luxA bacterial luciferase.While the novel variants showed comparable luminescence to naturalvariants, they differed in 18–35 mutations to the closest naturalprotein, showing the capability of VAEs to generate functional variantsin unexplored parts of sequence space.VAEs havesuccessfully been applied to the protein space as well, creating novel,functional enzymes. Hawkings-Hooker et al.78 used a similar VAEmodel to generate novel variants of the human ornithine transcarbamylase(hOTC) enzyme. Again, without explicit information about sequenceproperties, the VAE generated functional variants with on averageeight mutations compared to the wild-type hOTC enzyme. The generatedvariants showed on average improved thermal stability as well as specificactivity in vitro compared to both the wild-typeand a variant based on the consensus sequence from 5000 OTC enzymes.Finally, the authors expressed VAE variants that performed well in vitro in HepG2 cells for in vivo testing,and while expression levels were on par or slightlyreduced compared to the hOTC wild-type, specific in vivo activity was improved in 7 out of the 12 VAE-generated variantstested.Giessel et al.Having seen that generative models like VAEs are capableof producingfunctional and even improved novel enzymes simply using sequence-inherentinformation, the logical next step was to try to bias these modelsexplicitly to generate variants with desired properties. This canbe achieved by coupling a model that predicts functional propertiesof an enzyme to the generative model. The predictive model can guidethe generative model toward highly functional regions of (latent)sequence space.79 combineda denoisingautoencoder (a model similar to a VAE) with a Gaussian process regressorthat predicts protein properties. Using a multiobjective Bayesianoptimization approach, they generated novel red-spectrum fluorescentproteins (RFPs) with improved predicted stability and solvent-accessiblesurface areas (SASAs). In vitro testing of thesenovel RFP variants showed that the authors’ model producedvariants with both higher melting temperatures and brightness comparedto any variant in the training data. While this latter applicationhas not yet been validated to result in industrially relevant improvementsto enzymes, using a multiobjective optimization approach while factoringin uncertainty in predictions, coupled with a generative model, isin our opinion a highly promising direction to focus on. While classicaldirected evolution is highly effective at optimizing enzymes for asingle property, it is much harder to use it in multiobjective optimizationsettings.Stanton et al.in silico generated variantsbefore testing them in the lab can be useful to avoid wasting resourceson nonfunctional variants. Johnson et al.80 evaluated computational metrics to assess the quality of in silico generated protein variants.While generative models have shown that they can indeedcome upwith functional enzyme variants, it is still hard to predict whichof the generated variants will actually fold and function. Pairedwith the fact that in real-world engineering applications the bottleneckis often the expression and assaying of variants in the lab, metricsto evaluate the quality of Two metricswere moderately predictive of enzyme function: ESM-1vlikelihood scores and ProteinMPNN scores. On the other hand, neithersequence identity to natural sequences nor AlphaFold2 residue-confidencescores (pLDDT) were found to be predictive of enzyme activity. Thisindicates that high sequence identity to natural variants or high-qualityAlphaFold2 structures do not necessarily guarantee enzyme functionality.72 using ensemblesof CNNs and Stanton et al.79 using a Gaussianprocess model, adding an uncertainty estimation to ML models is highlyuseful to maximize the chances of success in finding high-performingvariants. Greenman et al.81 did a comprehensiveevaluation of various uncertainty quantification (UQ) methods forprotein sequence–function prediction.As briefly touched on in theexamples of Gruver et al.Having a measureof uncertainty in ML model predictions can help identify regions ofthe sequence space where the model is underconfident. Following theexplore–exploit trade-off, a balance between testing variantswith a high certainty of performing well and variants with a highuncertainty of their performance  can be struck. This way, proteins in underexploredregions of the sequence space can be sampled while still continuouslytesting variants with a high probability of success.82 and can similarly be applied to protein engineering.85Bayesianoptimization is highly effective in striking this balancewhile being able to also incorporate prior knowledge about the problem,constraints to the search space, and the ability to optimize multipleobjectives simultaneously. This has successfully been used in chemicalsynthesis engineering to reduce the number of experiments requiredto find the optimal conditions for a reaction86 set out to engineer enzymes toward improved activity on acyl-ACPsubstrates. Using gene shuffling to sample sequence space and Bayesianoptimization-guided machine learning to identify optimized sequences,they identified an enzyme displaying twofold higher in vivo fattyalcohol titer than the best natural sequence tested. They performeda total of 10 design–build–test–learn cycleswhile only testing 10–12 sequences each round. Hu et al.87 achieved a 4.8-fold improvement in the selectiveproduction of a rhamnolipid congener by optimizing the RhlA enzymeover four design–build–test–learn cycles againleveraging Bayesian optimization for efficient sequence space exploration.Integrating the aforementioned methodsinto iterative design-build-test-learncycles can help in continuously improving protein engineering effortsover rounds of engineering. Using Bayesian Optimization in both candidateselection and experimental design, these cycles enable the systematicexploration of the protein sequence space and facilitate the discoveryof novel protein variants with desired properties. By iterativelyrefining the models based on experimental feedback and adapting theexperimental setup to constraints such as batch sizes and screeningbudgets, the efficiency and effectiveness of the protein engineeringprocess can be enhanced. Greenhalgh et al.88 Thesynthesis of highly sought-after products often requires chemicaltransformations for which no analogs can be found in nature. For such“xenobiotic” chemical transformations, the space ofnaturally occurring enzymes is inherently a limiting component. Thegeneration of enzymes outside of the evolutionary toolbox requirestheir design from scratch based on the biophysical principles thatguide the desired chemical reaction. This task is called denovo enzyme design. Its feasibility was elegantly shown, e.g., by Hilvert et al. in the late 1980s by engineeringcatalytic antibodies to catalyze a Diels–Alder reaction89 or the de Grado and Dutton groups in 1994 withtheir design of multiheme-containing proteins for redox reactions.90 With the development of the physics-based Rosettasoftware package,91 the first novel “xenobiotic”enzymes such as aldolases92 or Kemp eliminases93 were designed. Yet, even though these enzymeswere active, the exhibited activities were below industrially relevantlevels and could not be meaningfully improved by computational meansalone. Apart from the limited understanding of enzyme biophysics,this failure was in our opinion most likely due to the low accuracyand speed of most methods in computational structural biology anda very limited set of suitable starting structures.State of the art machine learning greatly assists the understandingof enzyme structure and function.94 and AlphaFold2,95 both of which leverage the power of transformerarchitectures. Parallel to these advancements, in the earlier yearsof ML protein design research, generative adversarial networks (GANs)and variational autoencoders (VAEs) were being explored as potentialtools for protein design.96 However, as the fieldadvanced, these methods were superseded by models with more capablearchitectures, like denoising diffusion probabilistic models (DDPMs).These models, known for their success in text-to-image generation,were able to leverage the “structural awareness” ofstructure prediction models for highly accurate generative protein-structuremodeling.99 Conditioning DDPMswith structural motifs extracted from native enzymes lead to the generationof high-quality protein backbones scaffolding the structural motifs.For the task of designing minimum-energy sequences onto generatedprotein backbones, models based on geometry aware transformers, suchas ESM-IF100 and ProteinMPNN,101 have greatly outperformed purely physics-basedsequence design methods on the metric of native sequence recovery.The geometry-based ProteinMPNN, which is built on a message-passingframework, was further experimentally validated to produce solubleand highly thermostable sequences. For the structure-free generationof protein sequences, deep-learning architectures used in naturallanguage processing (for example GPT) were trained on protein sequencedatabases to learn the “grammar” of evolutionary andstructural information encapsulated in protein sequences,60 ESM2.102 The resultingprotein language models were capable of producing meaningful representationsof protein sequences62 that could be usedfor downstream structure prediction or variant-effect-prediction taskswithout needing to finetune the models on specific enzyme familiesor folds.63 Generative protein languagemodels like ProtGPT2103 and ProGen104 were successfully employed to generate novelenzyme variants guided by enzyme-family descriptors. A comprehensiveoverview of most recent deep-learning based protein design methodscan be found at.105ML most noticeably impacted computational structural biology in 2021with the introduction of the highly accurate and fast protein-structure-predictionnetworks RoseTTAFoldde novo is not clear yet. The strikingimprovement that deep learning brought to the speed and accuracy ofprotein structure modeling is still pending for the task of modelingstructure and dynamics of protein ligand complexes. For example, DiffDock,the current state-of-the-art method to predict small-molecule binding,could predict only 38% of protein–ligand complexes in the PDBinddatabase to an RMSD of less than 2 Å.106 Yet, fully automated de novo design of enzymeswill likely depend on deep-learning methods that thoroughly modelthe behavior of small molecules. We think that the ability of thesemethods to represent ligands in the context of enzyme active sitesat atomic resolution will be essential to account for, e.g., dynamic and electrostatic effects that drive the high catalyticactivities found in natural enzymes. Currently, the training of suchall-atom models is hampered by a lack of high-quality data sets thatcontain protein–ligand complexes in conjunction with dynamicand enzymatic information. The training of highly accurate modelsthat can predict small-molecule behavior thus requires meticulouscuration of the available experimental data. The MISATO data set bySiebenmorgen et al. is one such example.107 The authors refined roughly 20000 protein–ligand complexesfrom the PDBind data set108 using semiempiricalquantum mechanics. Next, they computed molecular dynamics trajectoriesfor all complexes, thereby joining high-quality structural informationwith dynamic descriptors. For the data set SPICE, Eastman et al. computedforces and energies of, in total, 1.1 million conformations for adiverse set of small molecules at the ωB97M-D3(BJ)/def2-TZVPPDlevel of theory.109 We suspect that deep-learningmodels trained on such high resolution/high level of theory, yet syntheticquality data may represent another route to outperform current physics-basedmethods in speed and accuracy for the task of modeling the behaviorof small molecules in the context of enzyme active sites.Deep-learningmethods readily demonstrated their effectiveness for designing proteinstructure. Whether this translates to an ability to design industriallyrelevant enzymes de novo enzymedesign requires combining multiple deep-learning methods with physics-basedmethods into comprehensive protocols. Yeh et al. recently employedsuch a combination to design artificial luciferase enzymes.110 Using an approach they called “familywide” hallucination, they constructed a diversified libraryof idealized NTF-2 scaffolds that enclose a binding pocket with shapecomplementary to their targeted synthetic luciferase substrates. Next,they employed the physics-based RifDock to extract proteins from thescaffold library that could hold the catalytic amino acids in therequired catalytic geometry. With their approach, they designed artificialluciferases that, after only one round of site-saturation mutagenesis,exhibited higher selectivity and activity compared to native luciferaseenzymes. Albeit successful, this composite approach requires expertiseand can be highly error-prone because most deep-learning based sequence-and structure-generation methods were not trained explicitly on enzyme–activitydata. An orthogonal yet not “truly” de novo approach for designing enzymes was recently described by Lipsh-Sokoliket al.,111 in which large fragments ofnaturally occurring protein structures were successfully recombinedto yield highly active xylanases. An in-depth analysis of the thusgenerated enzymes revealed several metrics that correlated significantlywith enzymatic activity. When the authors trained a “simple”ML model to capture the correlated features, they found that it canbe used as an activity predictor for their design approaches. Sucha strategy would in principle be feasible for any reaction envisionedand thus highly attractive for industrial applications. In our opinion,making de novo enzyme design industrially relevantwill require rigorous testing of structure–function relationshipsof enzyme catalysts with iterative design-build-test-learn cycles.Nevertheless, we can imagine de novo enzyme designto be a viable addition in the biochemist’s toolbox over thecourse of this decade.To date, because ofthe lack of deep learning methods that accurately depict structure–functionrelationships for small molecules, Biocatalysis and enzyme engineeringin particular are multidisciplinaryscientific topics involving molecular biologists, biochemists, computerscientists, chemists, and engineers. Communication among technicalexperts, especially between experimentalists and computational scientists,is of crucial importance to clarify goals and avoid failures in experimentaldesign, data interpretation, and the use of wrong methods. Educationon how to effectively communicate across disciplines will play animportant role as continuous growth of ML applications in enzyme engineeringis to be expected. This aspect goes beyond the technical experts workingtogether on a project, but also reaches out to funding agencies andpublishing sphere, where it is difficult to find reviewers with sufficientlybroad expertise.Similarly, the need to not just describe theresults but betterexplain the reasons why a particular method has been chosen and howit compares to other ML methods would be very valuable for the communityof readers.The utilization of more complex computational modelsfor enzymeengineering raises the bar for the level of expertise required tooperate these models effectively. Particularly in an industrial setting,the sufficient availability of skilled ML or deep-learning engineersmay not always be guaranteed. The application of advanced models necessitatesnot only a deep understanding of the underlying algorithms but alsothe ability to fine-tune these models based on the specific task athand, interpret the results accurately, and troubleshoot any issuesthat may arise. Furthermore, the development of novel models or techniquesrequires a high degree of creativity and inventiveness, which is askill set that may be even rarer. Therefore, while the potential benefitsof employing more powerful models are significant, organizations mustcarefully consider the trade-off. The investment in highly skilledML engineers to operate and innovate with these models can be substantial,and it is crucial to ensure that the potential gains in predictivepower and efficiency outweigh these costs.de novo computational design of enzymes is not yet a reality, the generationof high-quality data for training ML models and the experimental validationof newly proposed variants are key components of the protein engineeringprocess. For example, although the biocatalysis community has accumulateda vast amount of enzymatic screening and reaction optimization dataover the past decades, it currently is impossible to harness the learningsof all the accumulated knowledge from the current electronic laboratorynotebooks, not to mention earlier records. More recently, there areconcerted efforts to establish standards in reporting of biocatalyticdata. This is critically important because without the ability togenerate, validate, and store machine readable data in the lab, theutility of even the most sophisticated computational models becomesseverely limited. Therefore, a balanced investment in both computationaland experimental resources is essential for the successful futureapplication of ML in enzyme engineering.In addition, it iscritical to have sufficient wet lab capabilitiesand data infrastructure. As long as fully Looking at the generallandscape of current ML models and the correspondingtrends, it is hard to overstate the importance of awareness of theincreasing speed of methodological developments in the field. Dominancecycles in the succession of popular models have been shortening, whilethe potential for general applicability has been increasing. Thisholy grail of generalizability lies at the heart of ML itself, andis one of the keys to success with these very powerful methods.de novo design. One distinctive strength of thecurrently emerging most powerful ML models is the possible integrationof many types of different data into extremely large multimodal models112 that develop a broader understanding of theunderlying mechanisms.It is only fair to expect drastic and even more accelerating developmentsin the coming years, also for the benefit of enzyme discovery, engineering,and in silico creation113 and optimization114 of protein bindersand small molecules.106 On top of modelingbinding behavior, predicting biocatalysis also requires a deep understandingof enzymatic mechanisms and corresponding chemical implications, makingit “a “tough nut to crack” and one of the mostchallenging areas for ML.ML has become an integral part of drugdiscovery, which is constantlyexpanding with the advent of innovative ML-based discovery tools.In particular, recent advances in generative ML models have facilitatedthe 115 will enhance the reproducibilityand reliability of results while also providing a larger, diversedata pool on which ML models can be trained. Additionally, it is crucialfor ML models to simultaneously target multiple properties, such asactivity, stability, selectivity, and solubility. By considering theseparameters concurrently, we can foster a more comprehensive understandingof molecular behavior, enhancing the accuracy of in silico designs and predictions. Further incorporating molecular dynamicsinto ML training can account for the dynamic nature of molecular systems,which is often not captured by static models. This integration couldlead to more precise predictive models, bringing us closer to therealistic modeling of molecular behavior.However, to more consistently applyML in these fields, severalscientific advancements need to occur. There should be stringent requirementsfor data collection and increased efforts to make data sets, alongwith corresponding codes, publicly available. Such approaches, suchas EnzymeML,Despite this complexity,the advancements in data structuring andcategorization can pave the way for ML-accelerated biocatalysis. Moreover,to facilitate widespread adoption, ML tools must be made more user-friendlyfor enzyme engineers, simplifying their utilization in real-worldscenarios. In this context, the complexity of enzymology does notpose an insurmountable barrier but rather presents an exciting frontierfor future ML advancements. Indeed, by strategically addressing theseareas, we can foster a more seamless and effective integration ofML into drug discovery and biocatalysis.


Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior of interface interactions and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with G proteins. Our results reveal a strong co-dependency in the formation of interface GPCR:G protein contacts. This observation indicates that cooperativity of GPCR:G protein interactions is necessary for the coupling and selectivity of G proteins and is thus critical for receptor function. We have identified subnetworks containing polar and hydrophobic interactions that are common among multiple GPCRs coupling to different G protein subtypes . These common subnetworks along with G protein-specific subnetworks together confer selectivity to the G protein coupling. This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components. G-protein-coupled receptors (GPCRs) form a superfamily of membrane proteins of critical importance to cellular function, and the largest group of drug targets . BindingThere are three thermodynamic properties of the residue pairs interacting at the GPCR:G protein interface that play an important role in coupling: (i) enthalpy of the interaction ,7, (ii) There has been an exponential growth of long-time scale MD simulations of proteins and protein complexes, providing rich information on their dynamic properties ,34–41. HOur goal is to use an interpretable unsupervised machine learning network-centered methodology, BN modeling, with the protein-protein interface (GPCR:G protein interface in this context) contacts as the nodes in the network model. We previously established BNOmics as a robust and scalable BN platform for analyzing large scale multimodal biomedical datasets . We haveThe workflow developed in this study to apply BN modeling to MD simulation trajectories is shown in 2AR: 3SN6 , 2 Gq  and 2 G1R: 6GDG ,61, 5HT1BR: 6G79  with an BR: 6G79 . To enabBR: 6G79  and CommBR: 6G79  .We first compared the common and distinct contacts observed in the three-dimensional structures of GPCRs bound to the three G protein subtypes with those identified from the MD simulations ,c. As shet al (1R receptor contacts two G protein residues (H5.12 and H5.15) while during the MD simulations the number of contacts increases to 4 persistent contacts (greater than 20% frequency in the MD simulations) made with residues H5.08, H5.12, H5.15 and S3.01. These persistent contacts made by the GPCR:G protein interactions in the MD simulations are provided in the On the G protein side, 77% of total interactions are in the α5-tip, compared to ~60% of total residue contacts in the Gs and Gq proteins . The numet al , using cet al , N termiet al . Each G 1R). Since the ICL3 is not resolved in other structures we will not be counting these contacts in our analysis. In summary, the number of GPCR:G protein interactions increase during the MD simulations showing that the G protein core is equally involved in interacting with the GPCR as the α5-tip of the C-terminus.The differences in the distribution of residue contacts among the different G protein subtypes also changed significantly from the static structures during MD simulations , e. For Given the increased repertoire of GPCR:G protein interactions, we wanted to identify the interactions that show high level of cooperativity using a network model. Using the GPCR:G protein residue contact fingerprint matrix generated for each MD simulation snapshot –S7, show2AR-Gs complex. Compared to other nodes, this node has a notably higher node strength due to the strength and number of its connections, thus making it crucial for the network structure  and allosteric dependencies (Cα atoms of GPCR and G protein residues more than 10Å apart). We then compared the proportions of allosteric vs neighboring edges within the Markov neighborhoods, the set of nodes that are directly connected to node of interest, of top nodes (having the most BN connections) of the three G protein subtypes . We foun2AR and A2AR, which means that probabilistic relationships of the nodes in subgraph are similar for these two systems. However, comparing joint probabilities of GPCRs coupling to different G protein subtypes, e.g., β2AR and A1R, reveals that there is a significant difference between their probabilistic relationships despite the same topology. This means that even though the subset of interactions between ICL2 and α5-tip is observed across all G protein subtypes, the relationship between contacts inside this subset is G protein dependent. In particular, the residues that formed the contacts in this common graph have been shown to be important for GPCR and G protein coupling selectivity  shows tDB: 3SN6 . We propUncovering the residues in the GPCR:G protein interface that confer G protein coupling selectivity is critically important to designing selective drugs. Ample number of prior studies have highlighted the importance of residues in the C-terminus tip of the G protein to be important in G protein coupling selectivity ,15,66,733 (M3R) and histamine H1 receptors (H1R), both of which show stronger affinity for Gq compared to Go. By analyzing the change in EC50 of the chimeras over wild type Go, the authors identified several key hotspot regions in the G protein structure that significantly contribute to the selectivity of M3R and H1R towards Gq . We also used a second criterion to choose highly cooperative residues in the G protein that are not conserved across the G protein subtypes . We calcAlthough it is known that cooperative interactions play a critical role in protein-protein interactions, there is a serious lack of methods to uncover these interactions. In this study, we have adapted Bayesian network modeling that has been used extensively in generalist multimodal network construction and dissection, to MD simulation trajectories to analyze the cooperativity among the pairwise GPCR:G protein interactions. The MD simulations starting from the three-dimensional structures revealed multiple additional interactions that were absent in the static structures, thereby enriching the comparison among different G protein subtypes. Our analysis revealed specific regions of GPCRs and G proteins that play critical roles in mediating the interactions between these proteins. Residues on intracellular loop 2 (ICL2), transmembrane helix 5 (TM5), and TM6 consistently formed contacts with the G protein in all studied complexes. This observation is in line with previous studies ,56,68,74BN analysis of MD simulation data revealed the GPCR:G protein contacts that are highly cooperative, as indicated by the size of their Markov neighborhoods. These interactions were primarily located on ICL2, TM5, and TM6 of GPCRs and in the core and C terminal regions of the G proteins. Notably, we found a cooperative contact subnetwork formed by residues in the ICL2 and the α5 helix of the G proteins that were common in the BN of majority of the systems studied here. Such common cooperative hotspots may be required for the recognition of G proteins by GPCRs irrespective of the G protein subtype. We also found multiple cooperative contacts involving the core and N terminus of G proteins that are specific to the different subtypes. These contacts may confer selectivity to the GPCR:G protein recognition through further interface stabilization.One important finding in this study is that the mutually cooperative contacts at the G protein interfaces are not always spatially close, thereby suggesting that cooperativity at protein-protein interacting interfaces may not be strictly mediated by structurally close contacts. A large fraction of the probabilistic Markov neighborhoods of highly cooperative contacts are formed by distant contacts, thereby indicating that allostery plays a crucial role in conferring cooperativity within protein-protein interface. We found that these cooperative allosteric contacts are distributed throughout the G protein interface suggesting that the entire interface acts synergistically in stabilizing the coupling with GPCRs. Finally, the cooperativity scores of individual G protein regions correlated well with the changes in GPCR coupling efficiencies obtained from chimeric G protein studies. This suggests that our computed cooperativity measures could be used to prioritize specific G protein regions for designing mutations and targeted therapeutics to modulate the selectivity of G proteins towards their cognate GPCRs.In conclusion, our study advances the understanding of GPCR:G protein interactions by combining network-based analysis and MD simulations. The results highlight the cooperative nature of these interactions and identify key residues and regions involved in GPCR:G protein coupling. The findings provide valuable insights into the dynamic behavior and selectivity of GPCR:G protein signaling, opening avenues for the design of selective drugs targeting specific G protein subtypes. Further experimental investigations based on the predicted residues and network models will contribute to a deeper understanding of the intricate mechanisms governing GPCR signaling and its therapeutic implications.2AR with mini-Gs:6GDG, A1R with Gi2:6D9H, 5-HT1BR with Go: 6G79, AT1R with Gq: 7F6G, H1R with Gq: 7DFL) or X-ray structures (β2AR with Gs: 3SN6). All mutations in receptors and G proteins were reverted to wild type residues using Maestro . All ligands were parameterized by ParaChem . Missinland.edu,, and hydland.edu,,78. The 2 position restraint. This restraint was then progressively reduced: first to 5 kcal/mol·Å2 and then diminishing to 1 kcal/mol·Å2 in decrements of 1 kcal/mol·Å2. Each decrement was accompanied by a simulation spanning 5 ns. The final phase of the equilibration comprised a 50 ns simulation, executed devoid of any position restraints. The final snapshot of the equilibration step served as the initial conformation for five production runs (each last for 1 μs), which were initiated with randomly generated velocities. The simulation pressure was coupled to a 1 bar pressure bath and controlled using the Parrinello-Rahman method  to evaluate the nature of pairwise intermolecular residue contacts between GPCRGPCR and the G proteins. This script allows the identification of various interaction types, including salt bridges (cutoff <4.0 Å between anion and cation atoms), hydrogen bonds (cutoff <3.5 Å between hydrogen donor and acceptor atoms and an angle <70° between donor and acceptor), van der Waals interactions (a difference <2 Å between two atoms), pi-stack contacts , and cation-pi contacts . All types of contacts were considered in this study, and the contact analysis was performed on the compiled 5 μs trajectory, in which the water and ions were subsequently eliminated. The atom selection groups (‘--sele’ and ‘--sele2’) were matched with the corresponding amino acid residues for each protein domain, either GPCR or α-subunit of G protein. Each GPCR and G protein pdb number was converted into a corresponding generic residue number based on the BW numbering and the Common G protein numbering. Transmembrane helices ends were adapted based on the BW numbering for all receptors, except for β2AR: two present residues of ICL3 were given BW numbering of 5×77 and 5×78. Custom Python scripts were used to conduct one-hot encoding, generating a binary fingerprint for each simulation, where “1” represents the presence of a contact between two residues in a specific frame, and “0” indicates its absence.We utilized the Python script library “GetContacts” . These scores were compared against the score of the original, unperturbed model . SensitiThe directed graphs obtained in BNOmics were converted into moralized graphs using following steps: 1) for each node in the directed graph, its parent nodes were identified; 2) for each set of parent nodes, an undirected edge was added between every pair of parents that are not already connected; 3) all directed edges in the graph were converted to undirected edges. The algorithm ensures that the resulting moral graph is undirected and that all original dependencies are preserved. However, even though edges in the BNs are weighted, we have considered all edges as unweighted in the moralized graphs. The Python library NetworkX was employed for graph manipulation and analysis.Using the moralization algorithm described prior, we obtained the largest common subnetwork  heatmap .Supplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6Supplement 7Supplement 8Supplement 9


The spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for target recognition, cellular entry, and endosomal escape of the virus. At the same time, it is the part of the virus that exhibits the greatest sequence variation across the many variants which have emerged during its evolution. Recent studies have indicated that with progressive lineage emergence, the positive charge on the spike protein has been increasing, with certain positively charged amino acids (AAs) improving the binding of the spike protein to cell receptors. We have performed a detailed analysis of dissociable AAs of more than 1400 different SARS-CoV-2 lineages, which confirms these observations while suggesting that this progression has reached a plateau with Omicron and its subvariants and that the positive charge is not increasing further. Analysis of the nucleocapsid protein shows no similar increase in positive charge with novel variants, which further indicates that positive charge of the spike protein is being evolutionarily selected for. Furthermore, comparison with the spike proteins of known coronaviruses shows that already the wild-type SARS-CoV-2 spike protein carries an unusually large amount of positively charged AAs when compared to most other betacoronaviruses. Our study sheds light on the evolutionary changes in the number of dissociable AAs on the spike protein of SARS-CoV-2, complementing existing studies and providing a stepping stone towards a better understanding of the relationship between the spike protein charge and viral infectivity and transmissibility. The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gave rise to an unprecedented global effort to collect and share the information of the virus’ ongoing evolution  with itsNumerous experimental, computational, and bioinformatics studies have analysed the influence of the observed mutations in the S protein on the viral function. Some of the mutations have been linked to an increased transmissibility of the virus, while others have been linked to changes in its binding to ACE2 . A partiTo study the effect of S protein charge on, for instance, its binding affinity requires significant computational effort and more often than not involves a certain degree of approximation , unless In general, there are six AAs that can (de)protonate and thus acquire charge: three of them can be positively charged (arginine (ARG), lysine (LYS), and histidine (HIS)), whereas three of them can be negatively charged (aspartic acid (ASP), glutamic acid (GLU), and tyrosine (TYR))—see also the Methods section. In contrast to the S protein, the number of dissociable AAs on the N protein does not show any significant increases or decreases with lineage divergence . Here, tAs While the overall number of dissociable AAs on the N protein changes less drastically , some trAs already mentioned, the total number of dissociable AAs can serve as a proxy for the amount of charge on the S and N proteins. The changes in the total number of positively and negatively charged AAs on the N protein as lineage divergence increases are, on the other hand, again far smaller compared to those of the S protein . HoweverCompared to the abundance of data on SARS-CoV-2, there is much less information available regarding the evolution of the S proteins of other coronaviruses. Nonetheless, we can compare the number of dissociable AAs on the S protein of most currently known coronaviruses based on their reference sequences. By analysing the number of dissociable AAs on the S and N proteins of more than 1,400 different SARS-CoV-2 lineages, we have shown that there is an overall tendency towards an increase in the number of positive AAs on the S protein with increasing lineage divergence, an effect that is not readily observed for the N protein . While tDetailed inspection of the exact AA composition of the S protein in different SARS-CoV-2 lineages showed that the main contribution to positive charge comes from additional LYS residues, which reached the largest number with BA.1 and BA.3 Omicron subvariants and the XD recombinant variant . Interesoverall increase in the positive charge of the S protein, making it stand out in this respect from among other known betacoronaviruses . Even haviruses . These caviruses , and conaviruses . More poWe obtained a list of SARS-CoV-2 Pango lineages from the CoV-Lineages.org lineage report  on 24 Nothe entire genome relative to the root of the phylogenetic tree, i.e. the start of the outbreak—from the global SARS-CoV-2 data available on Nextstrain  in xtstrain . We haveN =1421.As the last selection step, we retained only those Pango lineages whose downloaded protein fasta file was not empty. We used these protein data to obtain the number of dissociable AAs on the S and N proteins. The numbers of dissociable AAs were then averaged over all available protein data for a given Pango lineage. The final number of analysed SARS-CoV-2 Pango lineages for which the entirety of the data described earlier was attainable is As a point of comparison, we also examined the number of dissociable AAs on the S and N proteins of known coronaviruses. We used the coronaviruses listed in the most recent Virus Metadata Resource (2 December 2022) issued by the International Committee on Taxonomy of Viruses  and limiOur final dataset of coronaviruses includes forty-six different species; of these, one is a repetition of WT SARS-CoV-2. The dataset also includes SARS-CoV, Middle East respiratory syndrome-related coronavirus, and all other four known human coronaviruses. In general, the dataset comprises nineteen species from the genus Alphacoronavirus, fifteen species from the genus Betacoronavirus, five species from the genus Gammacoronavirus, and seven species from the genus Deltacoronavirus.We analysed the S and N proteins of SARS-CoV-2 variants and coronaviruses to obtain the (average) numbers of six dissociable AAs: GLU, ASP, TYR, ARG, LYS, and HIS. We used Biopython to parse the protein fasta files and count the number of dissociable AAs on the S and N proteins. Three of the six AAs can carry positive charge , while the other three can carry negative charge  . HIS typ


Protein model refinement a the crucial step in improving the quality of a predicted protein model. This study presents an NMR refinement protocol called TrioSA  that improves the accuracy of backbone/side-chain conformations and the overall structural quality of proteins. TrioSA was applied to a subset of 3752 solution NMR protein structures accompanied by experimental NMR data: distance and dihedral angle restraints. We compared the initial NMR structures with the TrioSA-refined structures and found significant improvements in structural quality. In particular, we observed a reduction in both the maximum and number of NOE (nuclear Overhauser effect) violations, indicating better agreement with experimental NMR data. TrioSA improved geometric validation metrics of NMR protein structure, including backbone accuracy and the secondary structure ratio. We evaluated the contribution of each refinement element and found that the torsional angle potential played a significant role in improving the geometric validation metrics. In addition, we investigated protein–ligand docking to determine if TrioSA can improve biological outcomes. TrioSA structures exhibited better binding prediction compared to the initial NMR structures. This study suggests that further development and research in computational refinement methods could improve biomolecular NMR structural determination. Protein structure determination is a fundamental task in structural biology, as it enables the understanding of protein function. Nuclear magnetic resonance spectroscopy (NMR) is one of the primary techniques widely used to determine three-dimensional (3D) protein structure. NMR spectroscopy uses the magnetic properties of atomic nuclei in a molecule to provide indirect information about its molecular structure and dynamics. This information is obtained by analyzing the resonance frequencies as prime information of the nuclei in the presence of a magnetic field. The experimental data obtained from NMR spectroscopy can be quite complex, and interpreting it requires extensive calculations and computational methods to reveal a 3D structure model .NMR spectroscopy has several advantages over other methods of protein structure determination. First, it can be used to study proteins in their solution state close to their native environment, which enables NMR protein structures to be determined under near-physiological conditions ,3, sometThe principal drawback of NMR in application to structural biology is the size limitation of molecules to be analyzed due to a serious decrease in sensitivity and resolution, in particular for high-molecular-weight proteins . This siHowever, recent advances in both hardware  and experimental design promise to allow for the study of much larger proteins by pushing the current size limit of protein NMR upward to over 100 kDa ,12. For As another essential advance, computational techniques have been developed to improve calculation, particularly refinement of protein structures, based on structural restraints empirically obtained by NMR, such as chemical shift information and interatom distance restraints derived from nuclear Overhauser effect (NOE) data. The most commonly used methods for NMR protein structure calculation and refinement include distance geometry, simulated annealing, and torsion angle dynamics . These mIn recent years, there has also been a growing interest in using deep learning to improve the accuracy of NMR protein structure refinement. AlphaFold  and RoseHere we present TrioSA, a comprehensive NMR refinement approach that combines NOE-derived restraints, torsion angle potentials, implicit solvation models, and simulated annealing to find the lowest global energy on the empirical force field and the fewest restraint violations. TrioSA is an ensemble refinement method that was built using widely used methods to improve the accuracy of NMR protein structure determination with Chemistry at HARvard Macromolecular Mechanics (CHARMM) . Distancfeatures . In the features . The TriWe developed a comprehensive NMR refinement protocol called TrioSA , which was constructed in several steps . First, The third step was to prepare the target energy terms for the NMR refinement protocol. The experimental NMR data were converted to the CHARMM restraint format. Torsion angle potentials were employed using the statistical torsion angle potentials (STAP) method to refine the tructure . The CHAtructure . Solvatitructure .In the final stage of our study, MD simulation with SA, was used to optimize the protein structures. The target energy term in the SA procedure incorporated various experimental potentials, including NOE direct restraint, torsion angle, implicit solvation model, and stereochemical potential. The SA algorithm searches for the global minimum in the empirical force field by increasing the thermodynamic free energy of the protein system, allowing it to escape the kinetic barrier and decrease the internal energy. The system was subjected to temperature cycles from 100 K to 1000 K, then cooled to 25 K to achieve convergence. The structural quality of the refined protein structures was assessed using geometric validation scores and analysis of NOE violations in the distance and dihedral angle restraints. The NMR refinement protocol is illustrated in The RECOORD database provides a comprehensive collection of optimized protein structures refined by simulated annealing calculations using the CNS and CYANA software packages . It contWe evaluated the effectiveness of the TrioSA protocol by comparing the geometric validation metrics for our 3752 refined protein structures with those for the CNS, CNW, CYA, and CYW datasets. A comparative analysis was conducted on a subset of 256 structures that overlapped between the RECOORD database, which contains a total of 545 structures, and the TrioSA refinement protocol list, which contains a total of 3752 structures. In our comparison of structural quality, we used several metrics, including normalized discrete optimized protein energy (nDOPE) , clash sThe nDOPE evaluates the quality of protein models. Using a training set of high-resolution protein structures, it ranks the quality of models generated by homology modeling or ab initio prediction methods. Normalization accounts for differences in protein length and measures how well models match known structural features. Lower scores indicate higher quality, while higher scores indicate misfolding or incorrectness.p value was obtained. However, when the analysis was conducted with only CNW, CYW, and TrioSA, no significant p value was observed (Our TrioSA-refined structure achieved the lowest nDOPE score (−0.95), outperforming CYW (−0.93), CNW (−0.93), CNS (−0.40), CYA (−0.18), and the original structures (−0.20) A. Explicobserved .In terms of clash score, the TrioSA-refined structure performed the best, with the lowest score of 3.09 and the lowest standard deviation, as shown in We observed the effects of solvation models on the clash score, which measures steric clashes or repulsive forces between atoms in the protein structure; solvation models take into account the interactions of solvent molecules with the protein, which may result in lower scores, indicating a more stable protein structure with fewer steric clashes.The Ramachandran score measures the quality of a protein structure based on the conformation of its amino acids. This score is derived from the Ramachandran plot, which shows the distribution of dihedral angles  restraints provide information about the distances and relative orientations of nuclei in a molecule, which can help validate refined protein structures. In the TrioSA protocol, we fitted structures to experimental NOE restraints. By analyzing the effects of refinement on NOE restraints, we can determine whether refined structures are significantly improved over the initial NMR structures and whether they agree with experimental data. In our study, we analyzed the effects of protein structure refinement on distance NOE restraints and dihedral angle restraints. We analyzed the maximum and number of violations in various constraint categories, including all-range, intraresidual, sequential, medium-range, and long-range, for 3752 NMR protein structures. The results for the maximum violated NOEs and distance root mean square deviation (RMSD) of NMR-derived protein structures were classified into all-range, intraresidual, sequential, medium-range, and long-range distances . We obseA comparative analysis was performed to evaluate the impact of TrioSA refinement components on structure quality. Five NMR refinement protocols were constructed using a combination of torsion angle potential (STAP), an implicit solvation model (GBSW), and SA, which are commonly used in protein structure refinement. The protocols were applied to 3752 TrioSA refinement datasets. The five protocols were named initial (no refinement), SA with GBSW (SA+GBSW), SA with STAP (SA+STAP), SA alone, and SA with GBSW and STAP (refined). Four validation assessments were performed on each protocol: nDOPE score, clash score, Ramachandran score, and rotamer normality .The nDOPE score, which provides a normalized energy value for each non-hydrogen atom in a protein structure model, was used to evaluate the protocols, with lower scores indicating a more accurate structure. The clash score, which measures the occurrence of unnatural atomic collisions, was used to evaluate the protein structure models. The results shown in The Ramachandran score was used to evaluate the backbone conformation of the protein structure models. The results presented in Rotamer normality evaluates the side-chain conformation in relation to the local minima of van der Waals and torsional potentials. This evaluation is critical to the accuracy of protein–protein/protein–chemical docking, as side-chain conformation plays an essential role in molecular interactions. The initial NMR protein structure had a rotamer normality of −4.80, indicating a negative outlier. The SA protocol slightly improved this value to −4.35, while the SA+GBSW protocol showed a moderate positive effect, with a value of −2.00. Notably, the SA+STAP protocol had a significant positive effect, with a score of −0.20, due to the inclusion of the The results of the geometric validation metrics show that the combination of torsion angle, implicit solvation model, and SA significantly improved the geometric quality of the protein structures. SA, as a global optimizer, had a significant impact on the nDOPE score and the clash score. GBSW also had a positive impact on the clash score, while STAP had a significant impact on the Ramachandran score and rotamer normality. The TrioSA refined protein structures showed superior geometric quality compared to the initial structures in all validation evaluations.Protein structure refinement and protein docking prediction both rely on accurate protein structures. Protein docking prediction involves prediction of the binding of one protein to another to identify potential drug targets or design new drugs. This prediction requires accurate knowledge of the three-dimensional structure of both proteins involved in the interaction. The TrioSA refinement protocol aims to optimize protein conformation to better satisfy experimental restraints and improve the overall quality of the structure.We performed protein docking simulations using AutoDock Vina  and usedThe MCC of the X-ray protein–ligand complex is 0.77, that of the initial NMR protein–ligand complex is 0.41, and that of the TrioSA-refined NMR protein–ligand complex is 0.49. The MCC for the TrioSA-refined NMR protein–ligand complex is 0.08 higher than for the initial NMR protein–ligand complex. Although lower than for the X-ray protein–ligand complex, our docking analysis highlights the importance of protein structure refinement in improving the accuracy of protein–ligand binding site predictions.Therefore, our analysis provides evidence of the importance of protein structure refinement in improving the accuracy of binding predictions. The increase in MCC value from the initial to the refined structures shows that even small adjustments can have a significant impact on the accuracy of binding predictions. Our results are valuable for facilitating drug design or optimization using computational techniques that rely on docking predictions.Our study indicates that the TrioSA refinement protocol is a highly effective method for improving the quality and accuracy of NMR protein structures. Our analysis considered various validation metrics, including nDOPE, Ramachandran score, rotamer normality, and clash score. The TrioSA-refined structures achieved the lowest nDOPE score, the lowest clash score, the highest Ramachandran score, and the highest rotamer normality, demonstrating the potential of the refinement protocol to provide excellent geometrical validation metrics. Moreover, our study compared the geometrical validation metrics of the RECOORD datasets with those of our NMR refinement protocol. The results revealed that our refined structure outperformed the others in terms of clash score, Ramachandran score, and rotamer normality .We examined the NOE violation of distance and dihedral angle to confirm the changes in structural quality after NMR refinement. Experimental NOE restraints provide precision and balance between the empirical force field and experimental data . Our resOur study demonstrated that the SA protocol significantly improved geometrical validation metrics of refined protein structures compared to their initial counterparts . NMR speIn this study, we investigated the impact of various refinement components on protein structure determination using five different NMR protocols. The SA+GBSW protocol, which is based on the solvation effect force of the solvent-exposed surface area for the nonpolar part with a surface tension coefficient, demonstrated the best clash score   algorithIn this study, we conducted a comprehensive analysis of 583 pairs of X-ray and NMR protein structures with identical amino acid sequences. From this dataset, we identified 143 pairs as protein–ligand complexes. To ensure a focused examination of robust complexes, we excluded ligands that were either too small or attached to the exterior of the protein. We then selected 14 protein–ligand pairs with pockets larger than 50 er study . Pck VMDIn the context of evaluating protein–ligand complexes, TP refers to correctly predicted binding events, TN refers to correctly predicted non-binding events, FP refers to incorrectly predicted binding events, and FN refers to incorrectly predicted non-binding events. We hope that this explanation helps to clarify the definition of TP, TN, FP, and FN used to calculate the MCC in the evaluation of protein–ligand complexes.In this study, we aimed to develop a comprehensive NMR refinement protocol called TrioSA . Our goal was to overcome some of the limitations of previous NMR refinement methods and provide more accurate and reliable NMR structures for various biological systems. To evaluate the effectiveness of TrioSA, we compared TrioSA-refined structures with those obtained using the RECOORD method. Our results show significant improvements in overall geometric validation metrics. Specifically, TrioSA improved geometric validation metrics such as Ramachandran plot outliers and rotamer outliers. In addition, TrioSA reduced NOE constraint violations such as maximum distance NOE violation, dihedral angle violation, and long-range NOE violation. These results indicate that TrioSA fits the experimental data better than previous methods. Our study highlights the importance of refining NMR structures using protocols such as TrioSA. By improving the accuracy and reliability of NMR-derived protein structures, TrioSA can facilitate further growth and development in NMR research. However, our study has some limitations that should be addressed in future research. For example, the TrioSA refinement protocol does not provide significant methodological advances compared to standard NMR structure calculations using molecular dynamics. Additionally, we did not conduct a comparison of TrioSA with other NMR refinement protocols. In conclusion, our study demonstrates that TrioSA is a valuable tool for NMR researchers. Its implementation can significantly improve the accuracy and efficiency of NMR structure determination. Future research should address the limitations of our study by testing TrioSA on more complex and diverse NMR datasets.