Undergraduate research experiences are increasingly important in biology education with efforts underway to provide more projects by embedded them in a course. The shift to online learning at the beginning of the pandemic presented a challenge. How could biology instructors provide research experiences to students who were unable to attend in-person labs? During the 2021 ISMB (Intelligent Systems for Molecular Biology) iCn3D Hackathon–Collaborative Tools for Protein Analysis–we learned about new capabilities in iCn3D for analyzing the interactions between amino acids in the paratopes of antibodies with amino acids in the epitopes of antigens and predicting the effects of mutations on binding. Additionally, new sequence alignment tools in iCn3D support aligning protein sequences with sequences in structure models. We used these methods to create a new undergraduate research project, that students could perform online as part of a course, by combining the use of new features in iCn3D with analysis tools in NextStrain, and a data set of anti-SARS-CoV-2 antibodies. We present results from an example project to illustrate how students would investigate the likelihood of SARS-CoV-2 variants escaping from commercial antibodies and use chemical interaction data to support their hypotheses. We also demonstrate that online tools  can be used to carry out the necessary steps and that this work satisfies the requirements for course-based undergraduate research. This project reinforces major concepts in undergraduate biology–evolution and the relationship between the sequence of a protein, its three-dimensional structure, and its function. For the past 4 years the annual Intelligent Systems in Molecular Biology (ISMB) meeting has hosted a hackathon related to developing and extending applications for iCn3D, a web-based software tool for visualizing and investigating molecular structures. The 2021 theme, Collaborative Tools for Protein Analysis, explored different ways to use iCn3D in research applications. By participating in the hackathon, we learned how to use new features in iCn3D to support undergraduate research.Several studies have documented the contribution of undergraduate research opportunities to the likelihood that students will complete undergraduate degrees in STEM majors . These dAlthough most CUREs are implemented in laboratory courses, much scientific research takes place outside of a lab. Accordingly, we have been working to develop research projects that can be carried out by students working from home. During the ISMB hackathon, new features were added to iCn3D to support molecular interaction and mutation analyses and sequence alignments . These nTo be successful, an on-line CURE requires the following components: a low-cost research tool that functions on multiple platforms , data, and an interesting research question that cannot be answered by a Google search. Ideally, the skills and knowledge gained through the project support the learning objectives for the course. The question of whether new variants of SARS-CoV-2 can escape antibody neutralization satisfies all these criteria.Since December 2019, there have been over 766 million cases of SARS-CoV-2 infections and over 6.9 million deathsThat spike protein mutations impact the virus’ ability to spread is unsurprising given its critical role in infection. The spike protein is a trimer, located on the surface of SARS-CoV-2. The receptor binding domain (RBD) of the protein alternates between two states, up or down . In the Over the course of the pandemic, several antibodies received emergency use authorizations (EUAs) from the FDA for treating or preventing infections with SARS-CoV-2 . MutatioThe combination of an important problem (the COVID-19 pandemic), existing data sets and freely available web-based analysis tools (iCn3D and NextStrain) make it possible for students to use current techniques to participate in research on anti-SARS-CoV-2 antibodies. To support this project, we developed a data set of structures from commercial antibodies and created protocols for obtaining spike protein sequences from SARS-Cov-2 variants, aligning those sequences to the spike proteins in the structures, and examining each amino acid change in iCn3D to predict the overall effect on antibody binding. In the CURE, students investigate the question of whether SARS-CoV-2 variants are likely to escape from an antibody. They identify mutations in the antibody binding site, compare the predicted chemical interactions between the antibody and amino acids in the original spike protein and those in the variant, and use the chemical interaction data to argue for or against the potential for immune escape.Several molecular structures are available for commercial antibodies complexed with the SARS-CoV-2 spike protein . AdditioOur example (PDB ID 7KMG) was obtained from the NCBI Structure databaseiCn3D is a web-based molecular modeling program first released in 2016  and freeTo implement the project with a class, an instructor would download the protocols , assign NextStrain.org, for analyzing the evolution of viral genomes  and opened the “Latest global analysis–open data” instance. We used the Color By option and chose Emerging Lineage to highlight new lineages in the phylogeny for SARS-CoV-2. In May 2023, the emerging lineages,  represented 333 sequences, with the number of sequences for each shown in parenthesis.To obtain a spike protein sequence from an emerging variant, we used a combination of We chose to look at a sample in the BA.2.12.1 lineage  by zooming in  and clicOnce at the NCBI, we chose the Graphics link  from theWe searched the NCBI to find 7KMG in the structure database, identified the protein chains corresponding to the heavy and light chains of the antibody and the spike protein, then clicked the “full-featured 3D viewer” button to open the structure in iCn3D. We recorded the chain names for the antibody heavy chain, antibody light chain, and the spike protein for later use, then, opened the File menu, chose “Align” and “Sequence to Structure.” A window appeared where we pasted information about the protein sequence that would be aligned and the chain in the structure that it would be aligned with. The accession number for the variant protein sequence (USM32594.1) was pasted in the Sequence ID field and the chain name for the spike protein (7KMG_C) was entered in the NCBI protein accession field. Three alignment options are available: BLAST, Global Smith-Waterman, and Local Smith Waterman. We chose “Align with BLAST.” A 3D view of the spike protein sequence aligned to the 3D structure of the spike in 7KMG and the sequence is shown .We used annotations to identify amino acids in the antibody binding site and spot mutations. To annotate the antibody, we opened the Select menu, chose “Defined Sets”, and clicked the chain names for the heavy and light chains (7KMG_A and 7KMG_B). We opened the Select menu again, chose “Save Selection”, and changed the name to “antibody”. To show both the antibody and the spike protein, we opened the Select menu, chose Defined Sets, and selected both the antibody and the spike protein (7KMG_C). Then, we opened the View menu and chose “View Selection” to see both the antibody and the spike protein .Next, we annotated the antibody binding site. We opened the Select menu and chose the spike protein chain (7KMG_C). Then, we chose “by Distance.” For the first set, we selected the antibody, left the distance at 4 Å, chose the spike protein (7KMG_C) for the second set, and clicked Display. Amino acid residues within 4 Å of the antibody were highlighted yellow.To identify mutations in the variant, we located the aligned spike protein sequences in the Sequences and Annotation viewer. We unchecked all the checked Annotation boxes to declutter the view and clicked the “Show All Chains” button to the locate the aligned spike proteins. As in the structure, amino acids in the antibody binding site were highlighted yellow. We scrolled through the aligned sequences and held the cursor over changed residues to obtain their position numbers. Two mutations, E484A and Q493R, were found in the antibody binding site .We evaluated the impact of spike protein mutations on antibody binding by using the mutation feature in iCn3D. The mutation option predicts chemical interactions between pairs of amino acids and provides information about interactions that would be shared and interactions that would change. We opened the Analysis menu, chose Mutation, and entered a mutation to investigate. It is possible to evaluate all the mutations at once but for students, it is more straightforward to interrogate the changes one at a time.To enter a mutation, the PD ID is listed first, followed by the chain name, the position, and the one letter code for the mutant residue. For E484A, this was 7KMG_C_484_A. We clicked the “Interactions” button to view the predicted change in both a 3D structure and a 2D diagram  and usedIn this example project, we identified two mutations (E484A and Q493R) in the LY-CoV555 antibody binding site for sequence USM32594.1, a spike protein from an Omicron sample in the BA.2.12.1 lineage. The 3D structure models  show resIn the case of Q493R, both glutamine and arginine are predicted to share contacts with E102 and R104. In terms of differences, Q493 contacts A103 and has a hydrogen bond with R104. Neither interaction would be present in the R493 mutant. With two more interactions for glutamine 493 than alanine, we predict that the Q493R mutation also disrupts binding. Again, this prediction is consistent with published work .Since the E484A and Q493R mutations prevent Ly-CoV555 from binding to the variant spike protein, Bamlanvimab, the Ly-CoV555 antibody drug, no longer neutralizes the SARS-CoV-2 virus. As a result, the drug no longer protects people from SARS-CoV-2 infection nor can it be used to successfully treat people with COVID-19.This project was piloted by students in a 5-week online bioinformatics course (Biol285) at Shoreline Community College during Fall 2021 and Fall 2022. Students in the course have a wide variety of backgrounds. Some are changing careers. Some have Bachelors’ degrees or advanced degrees from non-US institutions. Some are the first members of their family to attend college, and some went straight to college from high school.We used this project with 13 students in our Fall 2022 course. Student evaluations noted the timeliness of the project and that they appreciated working on a current and important topic. All students were able to complete modules 1–4  but onlyThis project meets the need for a computational undergraduate research project that can be embedded in a biology-related course. NextStrain, the NCBI databases, and iCn3D are free, easily accessed, and available to any student with a web browser and an internet connection, allowing the project to be implemented at a wide variety of educational institutions with few upfront costs. The process for using these tools can be applied to other pathogens allowing a quick response to the next pandemic.CUREs require research questions that can be investigated by students in a limited time. Instructors require CUREs that discourage cheating and have results that can be assessed. This project meets those criteria. Every student (or team) is likely to research a unique combination of a sequence variant and an antibody. The evolution of SARS-CoV-2 continues to produce new spike protein variants (333 as of May 2023), making it unlikely that two students would choose the same variant from NextStrain and be assigned the same antibody by the instructor. Students may be able to search the literature and learn if mutations allow immune escape, but they are less likely to find detailed descriptions of the chemical interactions between a specific antibody and a mutant and be able to count the number and types of interactions that change. They would also be unlikely to find a shareable URL for their annotated structure model that they could hand in with their report. In summary, the requirement to investigate all the molecular interactions between amino acids in the antibody and those in the spike protein, combined with the large number of genome samples and combinations of antibodies with different variant proteins, make it unlikely that students will be able to solve their research problem using Google.The global pandemic emergency has been declared over by the World Health OrganizationCarrying out this project helps students meet common biology learning goals. Unlike other molecular modeling programs, iCn3D makes the relationship between protein sequences, their structure, and their function explicit. The ability to identify molecular interactions and view them in both 2 and 3 dimensions reinforces the importance of chemical bonds and their contribution to protein interactions. Integrating genomics resources with molecular modeling is a multi-omics methodology that drives home the important relationship between sequence, structure, and function.


The complexity of these systems remains a considerable challenge, but there have been dramatic improvements in sample handling, data acquisition, and data processing. Here, we summarise these developments and describe the paths towards comprehensive and comparative structural interactomes by cross-linking mass spectrometry.Cross-linking mass spectrometry has become an established technology to provide structural information on the topology and dynamics of protein complexes. Readily accessible workflows can provide detailed data on simplified systems, such as purified complexes. However, using this technology to study the structure of protein complexes  The study of protein–protein interactions is essential to understand how cells function and for developing effective treatments for disease. For this, the bridging of different levels of detail, from system-wide protein interaction screens to structures solved at near-atomic resolution is necessary. Cross-linking mass spectrometry (cross-linking MS) is one technique that has the potential to bridge these scales.in vitro [in situ. While the range of possible applications of cross-linking MS ranges from single proteins to whole tissues, the more complex samples still present significant technical challenges.Cross-linking MS is a structural proteomics technique that can produce topological information on proteins at single residue resolution. Due to its compatibility with heterogeneous and lowly abundant samples, it can be applied in a wide variety of contexts. It is this versatility for protein structure analysis that has made it an important part of the integrative structural biology toolkit ,2. Crossin vitro . HoweverThe basic principle of cross-linking MS is that nearby protein residues can be cross-linked together and after digestion, those cross-linked protein residues can be identified, thus revealing proximity in the original sample. The maximum length of the cross-linking reagent acts as a distance restraint that informs on the 3D structure. An experiment comprises cross-linking reaction, protein digestion, enrichment of cross-linked peptides, mass spectrometric acquisition, database search, and error rate control. The described pipeline comes in many flavours and like all mass spectrometry-based technologies has been subject to significant improvements in recent years .in situ, and assess how close we are to comprehensively map the structural interactomes of cells and tissues using this technique.In this review, we will discuss how cross-linking MS has overcome difficulties found when studying protein complexes in situ cross-links.Several ambitious studies have attempted to produce interaction maps in intact cells using cross-linking MS with significant investments of mass spectrometry resources. They have found that identified cross-linked peptides are highly biased towards the most abundant proteins and protein interactions  Figure . This isFirst, cross-linked peptides are sub-stoichiometric compared to the ‘background’ of un-cross-linked peptides. Indeed, it has been shown that cross-linked peptides make up a tiny fraction of the digested mixture, as low as <0.1% by ion intensity ,18 Figu. This isSecond, the fragmentation spectrum of a cross-linked peptide is a convolution of peaks from two peptides. Specialized matching software is used to identify these two peptides from this fragmentation data. For purified complexes the number of potential cross-linked peptide pairs is relatively small so weaker spectra can still provide enough information for a confident match. As the number of possible peptides in the database increases, their pairwise combinations increase quadratically (n-squared problem) D. SpectrSeveral studies have attempted to generate entire cellular or organellar interactomes by cross-linking the entire proteome for digestion and analysis. While this approach allows for the most novel interactions to be discovered, the starting material is as complicated as is possible. Depending on the particular target, the easiest way to deal with the overwhelming cellular complexity is to enrich the interactome of interest from the cross-linked cells before digestion, reducing the number of proteins to be considered . Studiesin situ studies . For simple purified protein complexes − where the goal is to produce as many cross-links to use as distance restraints for structural modelling – cross-linking MS protocols allow for several cross-linker chemistries and features. However, for analysing a protein complex in cells, the cross-linker needs to be able to cross a cell membrane and have very specific reactivities to limit the potential combinations of cross-linked residues, while simultaneously retaining the native structure. Depending on the complexity of the sample, the cross-linker may require additional functionality in its spacer that allows specific enrichment of cross-linked peptides or aids identification in the mass spectrometer.The choice of cross-linker will affect many aspects of the downstream processing and is an important consideration for in situ studies. Ideally, a cross-linker would react with any residue and would therefore provide the most varied structural information. However, such an unspecified cross-linker would cause a huge search space problem for identification even for simple samples. Instead, for simplified systems, a compromise is to use a hetero-bifunctional cross-linker such as SDA , which has an NHS-ester on one side and an ‘unspecific’ diazirine on the other side, which is activated with UV light. The UV-activated carbene is active for a very short time and therefore produces many fewer structural artefacts than homo-bifunctional NHS-esters [The most common cross-linkers for large-scale studies are homo-bifunctional and use N-hydroxysuccinimide (NHS) esters as the reactive groups. NHS-esters are highly reactive with lysines and the N-termini of proteins, with some activity towards serine, threonine, and tyrosine ,26. TheyS-esters . HoweverS-esters . TranslaS-esters ,31. ThesAs mentioned, one of the fundamental challenges in cross-linking mass spectrometry is to enrich the cross-linked peptides enough so that they can be selected for fragmentation in the mass spectrometer. Offline enrichment is common, with size exclusion chromatography enriching on the larger Stoke’s radius of cross-linked peptides , and strA much more specific approach is to produce trifunctional cross-linkers that contain an additional reactive group that aids the enrichment of cross-linked peptides after protein digestion . Cross-lFinally, cross-linked peptides can be enriched in the gas phase after injection into the mass spectrometer. There have been studies showing that the physicochemical properties of cross-linked peptides allow for them to be separated from other species using the FAIMS device ,58 or ioAs in standard proteomics, the database search software generates theoretical spectra from protein sequences and compares them with the experimental spectra in order to identify peptides. Therefore, the LC-MS acquisition strategy, the database search strategy, and the subsequent error estimations are all intimately linked and need careful consideration when handling complex samples.A common problem with the identification of cross-linked spectra matches, especially from large databases, is the unequal fragmentation of the two linked peptides, often with one fragmenting very poorly . A methoThere are other options that have been suggested to improve the information content of the fragmentation spectra. Each MS1 precursor can be fragmented by multiple complementary MS2 fragmentation methods to combine their information for database search ,70. EachThere are numerous different successful software solutions currently available that use different methods for identifying cross-linked peptides from fragmentation spectra, too many to review here . HoweverFinally, bespoke visualisation software has been produced to aid the interpretation of all of the different levels of cross-linking MS data. These include matched spectra, protein interaction networks, and residue-level data that contains the structural information to be mapped on structures ,80. EachCross-linking MS studies on proteins in their native context usually have the goal of an unbiased survey of protein interactions. Protein interaction screens are important for discovering novel biology and many such screens have been performed with techniques such as two-hybrid methods, affinity purification MS, proximity mapping, or co-fractionation MS. These other technologies have provided us with the majority of the PPIs we know today. However, they each have their limitations and so cellular interactomes remain incomplete. Cross-linking MS has unique advantages for protein interaction discovery; it can investigate any part of the cell, and labile interactions can be fixed prior to analysis.in situ cross-linking MS of whole bacteria [Several studies have now demonstrated bacteria ,20,81–84bacteria ,85–88, pbacteria , mammalibacteria ,56,90,91bacteria . Howeverbacteria ,20,81–84bacteria ,20,82.in situ. Cross-linking MS not only discovered this interaction but also provided distance restraints to allow the likely interaction surface to be modelled [If we look at the mitochondria cross-linking MS studies of, an example of an interaction that was unlikely to be discovered by more traditional PPI-mapping technologies is apoptosis-inducing factor 1 binding to complex IV of the respiratory chain ,93. Thisin situ. Too little cross-linker and the cross-links will be undetectable, too much and the proteins become challenging to digest, presumably as the very interconnected proteome provides steric hindrance to proteases. It may be that there is no optimal amount of cross-linker to detect cross-links on all proteins in cells, so multiple concentrations will have to be investigated [So far almost all studies have been performed with homo-bifunctional NHS-ester cross-linkers dissolved in solvents like DMSO or dimethylformamide and added to the cells. However, it has been shown that NHS-ester cross-linkers fail to penetrate far into large cells before reacting, leaving much of the cell non-cross-linked . On the stigated .The ‘static’ interaction maps generated in the studies mentioned above can suggest potential functions of the identified PPIs based on previous knowledge. A future challenge will be to adapt this approach to yield quantitative estimates of how PPIs change in different cellular conditions. Comparative or ‘quantitative’ cross-linking MS is a very powerful tool to investigate protein conformation changes in purified protein complexes  but is sin situ data is much more limited. Still, this structural information often implies structure–function relationships that can allow targeted validation experiments to be designed [As we have discussed, the advantage of cross-linking MS over other interaction methods is that identified links contain structural information in addition to the reported interaction. Cross-links can be used as distance restraints in protein structure modelling or validation as the spacer dictates the maximum distance between the two cross-linked residues. In studies on purified complexes, this information has helped model building into cryo-EM maps by assigning densities, threading novel folds, assigning registers and mapping interfaces . How onedesigned ,102.in situ cross-linking MS can roughly localize novel interfaces [A great deal of structural information already exists for many cellular complexes, so even sparse distance restraints generated from terfaces . If therterfaces , HADDOCKterfaces , or Roseterfaces , to prodin situ, in order to study the structure and function of molecular machines in their native environment. A combination that has proven especially powerful is the integration of cross-linking MS with cryo-electron tomography (cryo-ET), which can generate structures of large protein complexes in their native states and sometimes reveal unexpected molecular architectures [Mycoplasma pneumoniae. This supercomplex had an unexplained density between RNAP and the ribosome that could be confidently assigned to the transcription elongation factor NusA with the integration of cross-linking MS data (In addition to integrative modelling approaches, the increased power of protein structure prediction in addressing multi-protein complexes mean that novel PPIs discovered by cross-linking MS can be structurally predicted, with predictions independently validated by fitting of models to the experimental distance restraints ,106,107.tectures . Howevertectures ,109. In  MS data . It is bAs we have discussed, while cross-linking MS for purified complexes is an established technology for deriving residue–residue distances, it is a much more difficult proposition to use it to map protein interactions in intact cells. The main limitation is the abundance range of the proteome and the relative scarcity of the cross-links formed. We have shown that with clever experimental design, current pipelines can produce exciting biological discoveries and such studies are becoming more ambitious. Moreover, there are still innovations used in other proteomics workflows that have yet to be integrated into cross-linking MS pipelines, such as an improved understanding of cross-linked peptide fragmentation patterns or elution time predictions. Further technological innovations across the pipeline will carry us towards the goal of rapid and comparative structural interactomes, where we know there is still much to be discovered .in situ is still a technological frontier.Unlike for purified complexes, cross-linking mass spectrometry for analysing protein interactions Several studies have overcome experimental and computational hurdles and made important discoveries about the structure of protein interactions in cells.in situ.Further developments will allow for comprehensive and comparative analysis of the structural interactomes of difficult-to-study complexes


The integration of these approaches provides a means of generating protein complexes with diverse molecular recognition properties as tools for biotechnology and synthetic biology.Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a synthetic platform for protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pre-trained protein language models to expand,  Integration of synthetic coevolution with machine learning enables prediction of remodeled protein-protein interfaces. In evolutionary biology, the concept of coevolution underscores the compensatory relationships between biological systems that occur as a result of evolutionary pressures. Coevolution refers to reciprocal changes that occur under selective pressures between pairs of biomolecules or living organisms to fine-tune functions. Charles Darwin introduced the concept of coevolution by observing the relationship between the length of insects’ proboscis and the size of orchids’ spur, which led him to predict the evolutionary changes of insects that could suck from the deep spur of Darwin’s orchid . By analIn vitro, co-selection by mixing separate libraries is limited by the inability to isolate discrete coevolved pairs from complex mixtures, thereby losing connectivity between the sequences of members of interacting pairs  of Z domain and its affibody binder, ZpA963 (PDB: 2M5A). This is a model system with an interface idealized through phage display (2 of the 1662 Å2 buried solvent-accessible surface area (BSA) on the two protein chains (2 = 0.8382) at submicromolar affinity range  with micromolar affinity to determine if we could affinity-mature the interface by coevolution  for five positions on the Z domain and hydrophobic amino acids  for four positions on the affibody ZSPA-1 (9) of the yeast library almost reached the theoretical nucleotide diversity (4.29 × 109). After six rounds of positive MACS and two rounds of FACS selections, more than 90% of the populations enriched into the upper right quadrant of flow cytometry dot plots (D of 1.99 nM for LL1.c1 (LIFFK/FILF) and 1.86 nM for LL2.c3 (LVLF/FIIV) compared to the original dimer (KD = 2.92 μM)  -S2G.To characterize the relationship between coevolved protein sequences in our screen, we visualized the sequencing data as a network. We used statistical enrichment to identify the sequence pairs with the strongest likelihood of binding from the enriched library sequencing data, based on the overall count of the individual sequences in the screen. We used a hypergeometric test see  to calcuThe sequence similarity network (SSN) is an efficient way to observe relationships among large sets of evolutionarily related proteins . We consIn addition to the SSN, we utilized another visualization method to depict the cross-reactivity profiles of the NGS data in our coevolutionary libraries. The Circos plot shows the pairwise relationships, highlighting the relative cross-reactivity and orthogonality of the Z-A and Z-B proteins in both LL1 and LL2 libraries -S6. We sWe next attempted to track the mutational pathways of specific coevolved pairs to assess how these dimer interfaces were diversified along the course of coevolution. We generated single mutational evolutionary pathways connecting the original sequence (QFLI/LVIF) with the prominent LL2 library mutants . First, A was mutated to Phe, to produce a less favorable binding enthalpy compensated by a more neutral entropy in specific mutant (LIFFK/FILF). Phenylalanine is often conserved in protein-protein binding sites, and aromatic residues frequently serve as anchor residues to mediate protein-protein interactions  of several Z domain-affibody pairs along an evolutionary pathway , and S9.ractions . The comproteins .We next verified by cleavage-capture assay the relative specificities of each Z-A sequence toward Z-B sequences from this evolutionary pathway . StartinA-31B) was consistent with known contacts in the 3D structure of the original pair, indicating that structural constraints are captured in the sequence coevolution. Next, we applied a direct coupling analysis framework to the unfiltered LL2 sequences, which constitute a larger and more complex library  in both LL1 and LL2 library mutants. Phe13B of the Z domain, which is a core residue of the central hydrophobic patch in the original dimer, was mutated to the smaller Ile or Val in both LL1 and LL2 library mutants, and this was compensated by mutation of the opposing residue Leu9A to the larger Phe (A-31B) mutated in a compensatory manner in both libraries. Interestingly, here Leu17APhe is rotated outward, accommodating Ile31BLeu to fill the cavity between the two proteins . All structures were solved at high resolution (ranging from 1.00 to 1.92 Å resolution)  and S2. rger Phe . We alsoproteins .A and W35B accommodate the library mutations at positions 9A and 32B by adopting completely different positions and local environments (A-32B) and proximal residues, Gln10A and Trp35B, kept close contact in all mutant structures, albeit with different interactions. The ability to rearrange at these positions allows decoupling of mutations despite close proximity  and bias specificities (KD of Z-ALL1.c4 (FILFK) with Z-BLL1.c4 (FIVM) = 2.53 nM, and with ZSPA-1 (LVIF) = 21.9μM).The extent of interface structural remodeling in all complexes due to the coevolution selection pressure is made clear in ronments . The tworoximity . These rA, 31B, and 32B) but have virtually no cross-reactivity with each other. Z-A sequences of LL1.c1 and LL1.c2 bind to their own Z-B sequence 500-fold stronger than when mixed with the other’s Z-B sequence   than A-LIFFK (B=15) from the LL1 library, and A-LVLF (B=53) and A-VFLV (B=42) are also more cross-reactive than A-IVFF (B=3) from LL2 library sequence data. To answer the question of what causes differences in the cross-reactivity of certain Z-A sequences and to clarify specificity-determining residues, we compared high-affinity mutant structures of each Z-A sequence . First, sequence . On the sequence . Thus, t Trp35B) . Due to d LL1.c2 . Taken tA-centered hydrophobic patch surrounded by multiple rewired hydrogen bonds, an additional hydrogen bond between Asn11A and Phe32BTyr, and α-helix 2 of Z-A was slightly shifted to generate new interactions with helix 1 of Z-B, which explains the significantly improved affinity between these two proteins compared to the original pair  (A and 31B) as LL1 mutants are also seen from LL2.c7 and c22 mutants (The monomers from the high affinity mutants from the LL2 library (three Z-A mutants and five Z-B mutants) are even more orthogonal to one another  and S2G.nal pair  and S14. Trp35B) . Additio mutants . ContrarSPA-1 = 0.640)  versus cross-reactive  complexes. All mutants except for one (LL2.c1) had a higher fraction of nonpolar BSA than the original dimer (58%), and all mutants except for one (LL2.c7) had a higher packing score (PackStat) than the original dimer (PackStat of Z/Z= 0.640) . The croThe large database of coevolved complexes led us to ask if this information could be used to inform predictions through machine learning. One limitation of our experimental screen was that we used a limited set of amino acid codons in our experimental screen in order to fully sample the diversity of the yeast display libraries. But this raised the question of how to predict the binding affinity of larger diversity libraries containing more diverse amino acids without exceeding the practical diversity limits of the screening platform. One solution is the use of protein language models, which are self-supervised machine learning models pre-trained on large protein sequence databases , 26-28. To determine if protein language models could be used to model Z-affibody pairs in our screens, we used the pre-trained ESM protein language model , which iWe first encoded the individual protein sequences from each protein pair in the LL1 screen and trained a deep neural network to identify positive interacting pairs. Positive interactions were defined as enriched or filtered protein pairs occurring significantly more often than expected in the enriched library (rounds 6 and 7), reflecting our methodology in the SSN and DCA analyses see . NegativFinally, we applied our LL1-trained model to all LL2 screening rounds from naïve to final round 8 to assess the ability of the model to predict interactions of pairs in different selection stages . The preA, was mutated to smaller amino acids like leucine or valine, Phe9B or Trp35B newly form the core of the central hydrophobic patch, presumably rearranging an existing hot spot or creating new ones. We infer that coevolving contact residues can fundamentally change binding interfaces to have different specificities and affinities by reinforcing or rearranging hot spots. The remodeling of the dimer interface of the Z domain and affibody was similar to the repacking of the hydrophobic core of widely-studied proteins such as Rop, T4 lysozyme, and λ Repressor-GCN4 Leucine Zipper Fusions . To express affibody monomers, the vector was transformed into E. coli BL21 (DE3), and the cells were grown at 37°C in TB medium supplemented with 50mg/l kanamycin. At 0.6 OD600, 0.5mM isopropyl-β-D-thiogalactoside (IPTG) was added to induce protein expression and the cell culture was incubated for overnight at 30°C before harvest. The proteins were purified by Ni2+-NTA agarose column chromatography  followed by size-exclusion chromatography with a Superdex S75 10/300GL Increase column . The proteins were stored in HEPES buffered saline . Affibody proteins used for surface plasmon resonance experiments were site-specifically biotinylated at the C-terminal BAP tag using BirA ligase and re-purified by size-exclusion chromatography.The DNA plasmids encoding for each affibody were cloned into pET28, a bacterial expression vector. The vector includes the affibody gene with either only C-terminal His6-tag or biotin-acceptor peptide tag  followed by HisS. cerevisiae strain EBY100  by fusion to the C-terminus of the Aga2 protein. Affibody dimers connected with a GS-linker and 3C protease cleavage site in the middle were inserted between an N-terminal cMyc epitope and a C-terminal HA tag. N-cMyc-ZA-linker-ZB-HA-C insert was cloned into the pCT302 vector (Addgene #41845). Competent yeast cells were electroporated with affibody plasmids and recovered in YPD  at 30°C for an hour. Next, recovered cells were grown in SDCAA media  to OD600 10, and the cultures were induced at 20°C for 24 hours by diluting to OD600 1.0 in SGCAA  . Antibody-stained cells were washed with MACS buffer , then incubated in 20 μL 3C protease cleavage solution (lab-made 3C protease was diluted to 0.4 mg/mL in MACS buffer) at 4°C. At each time point, 2 μL was sampled and diluted in ice-cold 100μL MACS buffer, and fluorescence was measured by flow cytometry. The measured mean fluorescence intensity (MFI) was divided by MFI before cleavage to gain % of max MFI to represent an affinity between two interacting proteins.For single clone cleavage-capture assay, colonies were picked from transformed EBY100 cells plated on SDCAA plate. 5 × 10Circos plots were created via the circlize software package . In shorSequence similarity networks and cluster graphs were created via the igraph software package . Nodes oTo measure the coevolution relationship among interface residues, we computed the mutual information (MI) between two positions ., 2005) , where fTo uncover direct coupling signals from the MSAs, we used a method based on the estimation of the inverse covariance matrix following  . For posSequence library filter with a 0.05 p-value threshold, resulting in 14,491 pairs. For naive samples, we randomly sampled 14,471 pairs from the naive library that were not present in the intermediate enriched library. We then randomly split the data into training and validation sets with 80% and 20%, respectively. For the LL2 library, we applied the same method, resulting in 2,794 enriched and 2,763 naive samples as our test set. We also normalized the sequencing counts for our training label such that all naive samples scored 0 and all positive pairs are scored according to their observed sequencing counts then Min-Max normalized asTo train our deep learning model, we assembled positive and negative protein-protein pair examples from the oligopeptide pair dataset from the LL1 library. For enriched samples, we filtered the intermediate enriched library and applied the hypergeometric test described in Note that we added 100 base counts to all positive pairs to distinguish them from the naive pairs after normalization.For each oligopeptide pair, we used the full chain sequence with the corresponding amino acid in the mutant position as sequence input to the protein language model for the latent vector representation generation. The vector representation is taken as the average position-wise embedding from the last layer of the protein language model with 1,280 dimensions. For each pair, we generate the sequence embeddings for each chain separately as We designed and implemented a 3-layer 2D CNN model with kernel size  and channel size  followed by a two-layer fully connected network to predict the binding score of the input oligopeptide pairs. The model takes the two-dimensional oligopeptide pair representation We also applied sigmoid transformation to the fully connected network output for scaling.All models are trained with squared L2 norm loss and the Adam optimizer with learning rate of Details of crystallization and structure determination are provided in D) of affibody dimers were acquired by surface plasmon resonance (SPR) using the BIAcore T100 instrument . Approximately 100 resonance units (RU) of biotinylated affibody were captured on a streptavidin-coated (SA) sensor chip (Cytiva), including a reference channel with an unrelated protein. HBS-P+ (Cytiva) was used for all SPR runs. All measurements were made with two-fold serial dilutions using 60-120 s association and 300-500 s dissociation at a flow rate of 30-50 μl/min. Regeneration was performed using 0.02% SDS or 0.1M glycine, pH 2.5 after each analyte injection. The sensorgrams obtained were either fit to the 1:1 binding model or the steady-state affinity model using the BIAcore T100 evaluation software.Dissociation constants .For isothermal titration calorimetry experiments, proteins were dialyzed overnight against HBS buffer. After dialysis, concentrations were measured using the BCA assay kit (Thermo Fisher). Titrations of all mutants were performed in a Microcal VP-ITC instrument at 298 K with ZYang et al-Supplement


However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at:  The design of proteins with improved stability and activity is a critical aspect of research in biotechnology and related fields. It holds the potential to revolutionize a wide range of applications , from deTwo widely used protein design approaches are directed evolution and computer-aided protein design. The former approach mimics the natural gene diversification and selection process and involves iterative rounds of mutagenesis, which create a library of mutants, and selection of mutants with desired functions . ComputeMachine learning algorithms have revolutionized the field of protein design, enabling researchers to generate novel proteins with improved properties more efficiently . Two curde novo protein design, DE-STRESS , called DuraPETase , a web application framework for Python. Starting from the File Upload page, the user uploads the PDB files of the designed protein and the reference protein structure. Alternatively, users can choose to provide amino acid sequences of the reference and mutant protein, and ENDURE will automatically predict their structures using ESMFold , which includes the welcome page, file upload section, and interactive visualizations. The UI is designed to be user-friendly and easy to navigate, allowing users to upload their protein structure file, prepare the structure, and run the energy breakdown and residue depth calculations. Since Streamlit is our web development framework, the front and backend are managed under the hood. We do include several analysis modules, which are either pure Python functions or Python functions that call other auxiliary executables. The names of important internal functions in the ENDURE app are written in The protocol consists of two main stages: 1) structure pre-processing actions and 2) structure analysis actions. Those actions are run on the File Upload page.The pre-processing is an important step in the ENDURE web tool as it ensures that the uploaded protein structure is in the correct format and ready for analysis. The pre-processing actions include cleaning of the PDB files to remove any ligands, ions, or water molecules, relaxing of the protein structures to remove any steric clashes or unfavorable interactions, and determining the mutations for the designed protein relative to the reference protein. Additionally, the PDB file is renumbered in this step so that the first residue in the file is at position 1. Relaxing the protein structures is the second important action in preparing the protein structures for analysis. This ensures that the protein structure is in a low energy state according to the Rosetta energy function , which crun function is launched with four parameters as input: the input PDB file name, the location to save the result file, the location to save the log file, and the file path of the Rosetta executable. The output of the protocol is converted to a downloadable CSV file using the convert_outfile function, which saves the CSV file as a dictionary in the current session state.The analysis actions include the energy breakdown (EB) calculations, which provide information about the energetics of each residue’s interaction in the two analyzed protein structures, and the residue depth (RD) calculations. These algorithms are powered by the Rosetta energy function  and the calculate_depth function.These actions are followed by the RD calculation, which uses the MSMS algorithm  from BioThe processing and analysis actions launched from the File Upload page are run in the background to prevent the UI from freezing, and their results are integrated into the interactive visualizations in the front end.The Interaction Analysis page allows users carrying out a comprehensive analysis of the energetic changes in pairwise interactions of single- and multi-mutant protein designs. The user can select and analyze interactions from different categories (A to F explained below) and from different physical interaction types  through different control parameters on the side bar. Individual residue pairs, affected directly or indirectly by the mutations, can be selected and displayed in interactive 3D visualizations.We defined six categories of residue pair interactions to identify the regions in the protein structures most affected by the mutations.Category A: residue pairs that are interacting in the reference structure have different interaction energy in the mutant, even though neither of the two residues were mutated.Category B: residue pairs that are interacting in the reference structure have one member replaced, resulting in a different interaction energy in the mutant.Category C: residue pairs that are interacting in the reference structure no longer interact in the mutant, even though neither of the two residues was mutated.Category D: residue pairs that are interacting in the reference structure no longer interact in the mutant because one member was mutated.Category E: residue pairs that are not interacting in the reference structure interact in the mutant, even though neither of the two residues was mutated.Category F: residue pairs that are not interacting in the reference structure interact in the mutant because one member was mutated.In order for ENDURE to detect the different interaction categories A-F and different physical interaction types, there are several functions implemented on the page that users can execute by clicking the Start Calculations button . Such functions perform a post-processing and filtering of the scorefile generated by the Rosetta EB calculations.energy_calc function is used to identify the essential changes in interaction energies between the mutant and the reference protein structure. It takes the outputs of the residue EB computation performed for the mutant and reference structures, the list of mutations between the reference and mutant, and a streamlit progress bar. The function calls several sub-functions to perform various interaction energy comparisons for each interaction category and physical interaction type. The former is managed by the interaction_analysis function and the latter is managed by the following functions: salt_bridges, disulfide_bonds, and hydrogen_bonds. As the names suggest, these functions calculate the energy differences for different types of interactions, such as salt bridges, disulfide bonds, and hydrogen bonds.The interaction_analysis function calculates the difference in interaction energies between a mutant and a reference protein structure for a given list of mutations. The interaction_analysis function processes the outputs of the per-residue EB performed on the reference and mutant structures. It calculates the differences in all single-body  and two-body  energies for all categories (A to F) between the two proteins.The In analyzing the interaction energy changes between the wildtype and mutant protein, it is important to distinguish between total energy changes and significant energy changes. The former is the sum of all energy changes, including those with a small value. However, since thousands of small changes can occur, it is possible for insignificant changes to mask chemically important changes. To overcome this issue, the significant energy change is calculated, which is the sum of only those interaction energy changes that exceed a minimum magnitude. In ENDURE changes that are larger than +1.0 Rosetta Energy Units (REU) or smaller than −1.0 REU are considered significant. By focusing only on significant energy changes, the mutations that are likely to have a significant impact can be more easily detected.For the REF2015 scoring function for soluble proteins, there is an approximate 1:1 correspondence of REU and kcal/mol . Howevertotal_energy_changes and significant_changes functions, respectively. These functions operate on a dictionary returned by interaction_analysis and sum all as well as the significant energy changes for each interaction category specified in an interaction list. Interactions from categories A-F can be selected from a list and the change for a given interaction type  can be furthered inspected.The total energy change for all interactions and the sum of the subset of significant changes, is calculated using the Mutation-induced energetic changes can have different effects on different layers of the protein structure. Therefore, we implemented a residue depth analysis and combined it with the per-residue energy breakdown analysis to distinguish changes occurring on the protein surface from those occurring in buried regions of the protein structure.The Residue Depth page of the ENDURE app allows the user to analyze and compare the effect of mutations on the energy and spatial location of residues. Specifically, the user can select a residue pair that displays a strong negative energetic contribution and visualize the interaction in the protein structure. By adjusting the threshold slider, the user can see which residues have a significant impact on stability and select the most promising mutations for further analysis. Once the user selects a particular mutation by clicking on a point in the scatter plot, the app displays a side-by-side 3D visual comparison of the residue in the mutant and reference structures, which allows for a direct comparison of the effect of mutations on the protein structure. This analysis provides valuable insights into the structural and energetic changes resulting from mutations.The Welcome page  is the fIsPETase enzyme (PDB ID: 5XJH) (m) of DuraPETase by 31°C compared the wildtype IsPETase  , which hD: 5XJH) . As can IsPETase . In addiIsPETase  and HotPIsPETase , using EIsPETase . The favIn addition to the overall energy comparison, ENDURE also provides detailed information about the specific amino acid interactions that contribute to the improved stability of the selected mutant. The interaction analysis feature allows focusing on and visualizing specific residue interactions in the protein structure, which help to rationalize the underlying molecular mechanisms contributing to the improved stability of the selected mutant. These features will be described next.The Interaction Analysis page of ENDURE enables performing a detailed examination of the energetic changes for residue pairs of different types see . With thIsPETase. This salt bridge isn’t present in the wildtype protein but is in DuraPETase—i.e., it belongs to interaction category F. IsPETase has an isoleucine (Ile135) at the same position, which cannot form a salt bridge. In addition, as shown in the left-hand table in the screenshot in For example, The different categories of interaction changes (A to F) allow the user to quickly identify the changes in interactions that have occurred and to focus their attention on the most important ones. For example, if a user observes that most changes are of type E or F, this might suggest that new interactions have formed, which could also significantly affect function. By providing this information, the Interaction Analysis page helps the user to quickly understand and prioritize the changes in interactions that have occurred and guide their efforts in protein design.IsPETase, Ile135 is located on the protein surface, indicated by a low residue depth value of ∼2Å , to residues that aren’t interacting in the reference structure but make interactions in the mutant due to a mutation (category F). These different categories of changes provide a useful way for the user to identify which mutations have the strongest impact on the protein structure, and consequently focus design efforts on specific areas of the protein structure.Compared to previous research, our web application offers a user-friendly and accessible solution for analyzing protein structures and interaction changes. Prior research in this field has typically been focused on developing computational tools for protein structure prediction and analysis  or the aIt is important to note that the tool is limited by the accuracy of the underlying computational tools and algorithms used for protein structure prediction and analysis. Additionally, the interaction analysis section is based on a static protein structure analysis. Accuracy in predicting mutation-induced energy changes could benefit from a model ensemble approach , in whicDespite these limitations, it represents a significant advancement in the field of protein structure and interaction analysis. The integration of computational tools into a user-friendly interface makes it possible for scientists outside the field of computational structural biology to quickly and efficiently analyze protein structures and identify potential areas for improvement. In future versions, ENDURE could be expanded to include additional features and improvements, such as the ability to examine other kinds of proteins, including membrane proteins and proteins with noncanonical amino acid modifications, through the incorporation of different energy functions. It could also be enhanced to consider ligand molecules in the design analysis, allowing the identification of designs with improved binding affinity. These features are currently planned for the next version of ENDURE, which will be released in the future. Additionally, the tool could be further developed to incorporate machine learning techniques to improve the accuracy of the analysis. With these advancements, ENDURE could become an even more powerful tool for protein design and analysis.In conclusion, the ENDURE web application provides a unique and accessible solution for analyzing protein structure and interaction changes and, in that way, represents a significant advancement for the field of protein design. Categorizing changes in pairwise interactions for different interaction types provides a straightforward way for the user to guide their protein design strategies. Integrating computational tools into a user-friendly interface makes it possible for a broader audience to quickly and efficiently analyze protein structures. The future direction of the research will focus on further developing the application to incorporate analysis on protein dynamics, support for non-standard amino acid residues, and application of machine learning techniques. Furthermore, a command line interface integrated in the front end is planned, which will help further customize some of the analyses.


Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals. Proteins are the major functional macromolecules in biology, which play critical and diverse roles in virtually all cellular processes and are involved in numerous human diseases, including cancers, neurodegenerative diseases, and diabetes ,2,3. A cNotwithstanding the remarkable successes of single protein structure prediction , the neeIn this short review, we summarize recent progresses in the development and application of deep generative models for biomolecular modeling, particularly those developed for generating dynamic structure ensembles of proteins in various contexts. We note that several outstanding reviews already provide in-depth discussions of how ML can be used to learn the energy landscape and collective variables (CVs), derive coarse-grained models, and generate protein structure ensembles ,57,58,59As illustrated in The second major class of functional dynamics include proteins that undergo large-scale conformational transitions between two or more major states, which can be triggered by a wide range of cellular stimuli, including ligand binding, PTMs, and changes in the solution conditions  ,65,66. FThe third and fourth classes of functional protein dynamics include proteins that can remain partially or fully disordered under physiological conditions ,74,75. TGenerative deep learning models are a class of neural networks that aim to learn the regularities of the training dataset and capture such regularities using appropriate probability distribution functions ,101. TheBriefly, autoencoders (AE) are unsupervised ML algorithms that use neural network (NN) architecture to compress input data into knowledge (latent) representations. As illustrated in A key application for ML in the generation of protein functional ensembles is to aid in the discovery of low-dimensional CVs that can distinguish important functional states, particularly those that could monotonically describe the transition between the states and are suitable for usage in enhanced sampling MD simulations such as umbrella sampling, adaptive sampling, and metadynamics ,59. Even12 and a small protein GB98. The results suggest that DESP can sample much broader conformational space compared to both conventional and accelerated MD within the same amount of simulation time. Importantly, unbiased probability distributions also could be uncovered. DESP is in principle generalizable to other proteins. However, additional work is required to understand how the quality of the latent space learned, as well as the choice of biasing potentials, affect the sampling efficiency. Further analysis is also required to evaluate how accurate and/or physical the additional conformational space sampled by DESP is. The apparent lack of reversible transitions in the DESP trajectories is concerning, which may reflect a critical limitation of using KL divergence directly to drive the sampling of new conformational space. Tao and co-workers [In the so-called deeply enhanced sampling of protein (DESP) method developed by Salawu , a neura-workers  have comTiwary and co-workers recently adapted reweighted auto-encoded variational Bayes for enhanced sampling (RAVE) for efficient sampling of protein loop conformations . RAVE isstakes function to modulate how rewards are attributed to different agents for discovering new states. The benefit of this multiagent formulation comes from two features. Conformations are labeled and utilized by the agent who discovers them, and therefore, each agent computes the rewards from different data points. Additionally, the agents share information within an action space to tell other agents what conformations they have already discovered. It was demonstrated that the multiagent REAP algorithm is more efficient in sampling the loop motion of Src kinase and driving large-scale conformational transitions of the transporter OsSWEET2b. The same multiagent formalism was shown to work well with other adaptive sampling techniques, including “least counts” and AdaptiveBandit. A key limitation of both single-agent and multi-agent REAP algorithms, though, is their dependence on user-defined CVs for prioritizing sampling. As illustrated in the works above, identification of such CVs can be highly nontrivial for protein conformational fluctuations in general. It is also not clear how one can reconstruct unbiased ensembles from REAP trajectories, a capacity which will be important for functional studies. The pros and cons of REAP and related adaptive sampling techniques remain to be thoroughly examined, especially compared to weighted ensemble methods that can recover both kinetic rates and thermodynamic stabilities of long-timescale processes [Reinforced learning (RL)-based algorithms ,117 haverocesses .Learning low-dimensional representations of protein conformational ensembles is of interest for more than driving MD sampling. In principle, if one can learn the latent space representation from a relatively limit set of conformations, , one could then sample in the latent space to directly decode and generate new high-dimensional structures of the same protein. The requirement here is that the latent space representation is smooth and continuous. Such a generative approach can be dramatically faster than MD simulations. Nonetheless, the complexity and sheer size of the conformational space should never be under-appreciated, even for small proteins. Functionally relevant conformational states are vanishingly small in comparison, and importantly, they can be well-separated in the Cartesian space. It is not entirely clear if the desired mapping of high-dimensional protein conformational space to a smooth low-dimensional latent space representation exists, or, if such a mapping can be reliably learned from an incomplete set of pre-generated conformations. Nonetheless, direct sampling of protein conformational space using ML-derived latent representation is a highly attractive strategy and has continued to attract intense interest in recent years ,122,123.13 and the protein calmodulin (CaM), the approach demonstrated modest success and could be most suitable for generating new initial structures for seeding additional MD simulations. Using pre-defined latent space is unusual and likely will be ineffective in general.The ability of using the latent space to generate physically plausible structures has been evaluated using a set of proteins of different sizes, topologies and dynamic properties . The resDegiacomi and co-workers  later deNres (number of residues). It was shown that only ~ the first 10% of the MD trajectories were necessary to regenerate the full ensemble of small IDP Q15, but the amount of training data required increased rapidly for larger IDPs such as ChiZ. The authors also evaluated the effects of latent space dimension and input features . The results showed that larger latent vectors do not improve accuracy and the choice of input coordinates does not significantly impact model performance. Curiously, evaluation of VAE performance has focused on the accuracy of regenerating individual conformers, as measured by root-mean-square-distance (RMSD). It would be more desirable to examine ensemble distributions of key local and global properties, such as overall size, residual structures, long-range contacts, etc. One could also examine the details of conformational substates using clustering and principal component analysis, in comparison to the available ensembles generated by much longer MD trajectories. Furthermore, the three IDP ensembles appear largely random and devoid of nontrivial local structures and transient long-range organizations. Yet, many biologically relevant IDPs, especially those involved in cellular signaling and regulation, are not random coils, and contain important residual structures [Success has also been demonstrated recently using VAE to learn the dynamic conformational space of IDPs using short MD simulations and then generate full disordered ensembles . Here, tructures ,125. As x) using a generative network and reweighting procedures. The generative network maps latent space samples from a simple prior distribution P(z)  to high-probability samples from the target distribution P(x)~ex)−u and internal (sidechains) coordinate sets. It was demonstrated that the eventual generator could sample a key X-O loop conformational transition of protein BPTI that occurs on a millisecond-timescale. Clearly, extension of Boltzmann generators for one-shot generation of dynamic protein conformational ensembles requires much additional work. It has been argued that training solely on the energy is unlikely to be adequate [An ultimate “one-shot” generative ML model would take the sequence of the protein and generate a full ensemble of the most relevant conformations. This is an extremely ambitious goal for proteins with high-dimensional and complex conformation space. Progress towards this goal has thus far been more limited ,127,128.Feig and co-workers recently described a conditional generative model for generating disordered protein conformational ensembles using the sequence alone as input . The modRecent breakthroughs in ML approaches have transformed the studies of protein structure and function. The problem of predicting a native structure of protein has essentially been solved. A key new frontier is expanding these ML approaches to help describe dynamic fluctuations and transitions of proteins that are crucial to its functions. For this, much effort has been dedicated to generative ML models such as VAEs and GANs . A commoIt is generally recognized that it is likely critical to incorporate physics towards generative ML models that may be more generally applicable to nontrivial proteins ,133,134.Deeper and more systematic understanding of if and how the complexity of the high-dimensional conformational space of proteins can be mapped to a smooth and continuous space of sufficiently low dimensionality. This may be investigated using ultra-long trajectories of a large set of folded and unfolded proteins, such as those from Shaw and co-workers, using the latest balanced atomistic protein force field , as wellConsistent and rigorous evaluation of the performance of generative ML models that is widely accepted and adopted by the community. At present, most developments are benchmarked using different custom examples and special test-cases. There is only minimal cross-comparison needed to rigorously establish the strengths and weaknesses of various approaches, such as over-fitting, computational cost, and interpretability. Again, ultra-long MD trajectories or highly-converged conformational ensembles generated from enhanced sampling simulations of proteins of different sizes, topologies and dynamic properties could be used as a standard benchmark set. Similar practices have been instrumental in the development of methods for predicting protein structure , proteinNew ML approaches for integrating information from diverse datasets, such as protein structures, sequence alignment, and MD trajectories, and incorporating physical principles of molecular interactions, such as various empirical protein energy functions. ML has been under accelerated development in recent years, with many exciting ideas emerging, such as large language models (LLMs) , deep trAt present, it is hard to clearly envision how generative ML models of protein conformational ensemble may look or how generally applicable these models will be across the spectrum of protein dynamics relevant to biological function . However