Site‐selective protein functionalization serves as an invaluable tool for investigating protein structures and functions in complicated cellular environments and accomplishing semi‐synthetic protein conjugates such as traceable therapeutics with improved features. Dual functionalization of proteins allows the incorporation of two different types of functionalities at distinct location(s), which greatly expands the features of native proteins. The attachment and crosstalk of a fluorescence donor and an acceptor dye provides fundamental insights into the folding and structural changes of proteins upon ligand binding in their native cellular environments. Moreover, the combination of drug molecules with different modes of action, imaging agents or stabilizing polymers provides new avenues to design precision protein therapeutics in a reproducible and well‐characterizable fashion. This review aims to give a timely overview of the recent advancements and a future perspective of this relatively new research area. First, the chemical toolbox for dual functionalization of proteins is discussed and compared. The strengths and limitations of each strategy are summarized in order to enable readers to select the most appropriate method for their envisaged applications. Thereafter, representative applications of these dual‐modified protein bioconjugates benefiting from the synergistic/additive properties of the two synthetic moieties are highlighted. Site‐selective dual protein functionalization serves as an invaluable tool for investigating protein structures and functions in complicated cellular environments and accomplishing semi‐synthetic protein conjugates. The methodologies, as well as the applications of dual‐functionalized protein bioconjugates are highlighted in this review. They play many essential roles in a myriad of biological processes, such as molecular transport, energy conversion, inter‐ and intramolecular signaling.1, 2 NIn this context, the past decades revealed significant progress in the development of new methodologies for site‐selective protein functionalization to install the desired functionalities at pre‐defined sites.9, 10 Nevertheless, along with the increasing demand for multifunctional bioconjugates to perform more sophisticated biological studies in vitro as well as in vivo, appending only one type of functionality to proteins is often insufficient to customize proteins for the desired applications. For example, despite the clinical success of ADCs, classical ADCs equipped with a single type of anticancer drug could still suffer from low efficacy, drug resistance, unfavorable pharmacokinetics, immunogenicity, and the inherent hydrophobicity of the drug, which greatly hampers their further in vivo applications.18, 19In this review, dual functionalization is defined as the incorporation of two different functionalities into proteins in a site‐selective fashion, which is accomplished either at two different amino acids (AA) sites or at a single AA residue at the protein surface Figure . In bothOwing to the emerging interest for addressing previously “unanswered” fundamental scientific questions as well as offering practical solutions for biomedical applications, site‐selective dual functionalization of proteins has seen rapid developments in the past ten years. In this review, we first summarize the synthetic strategies for protein dual functionalization to provide a timely overview of this burgeoning field, which serves as a guideline for the selection of the most appropriate method for the envisaged applications Figure . In addi2Site‐selective protein functionalization either relies on the functionalization of canonical AAs on the protein surface or genetically encoded noncanonical AA (ncAA) bearing the appropriate bioorthogonal groups.Alternatively, the attachment of a multifunctional linker containing two reactive orthogonal groups to a single site on the protein surface, usually an exposed cysteine or a disulfide bond, also provides straightforward access for the incorporation of two different functionalities. These bioconjugation reagents include maleimide derivatives,−1 s−1, which is about 30 times faster than the SPAAC reaction.6 M−1 s−1), high bioorthogonality, catalyst‐free conditions and good biocompatibility.The recent advances in bioorthogonal chemistry have greatly promoted the progress of site‐selective protein modification. Nowadays, various bioorthogonal reactions have been reported with optimized reaction parameters, such as reaction rate, catalyst type, and substrate stability, to impart the desired functionalities for the envisaged applications.75, 763Most synthetic strategies for protein dual modification target canonical AAs exposed at the protein surface. These residues are immediately accessible without the need for tedious genetic engineering of recombinant protein variants and also mitigate the risk of negative effects on proteins folding and function. In this section, dual functionalization at two different sites as well as at a single site is discussed.3.1Direct modification of two rare AA residues on the protein surface is a straightforward approach to achieve dual modification of proteins. The combination of two different mono‐functionalization methods requires stringent selection to ensure orthogonality, compatibility and preferably high modification efficiency. For example, Gaunt and co‐workers developed a method based on chemoselective labeling of a single methionine residue with a hypervalent iodine reagent.An alternative strategy for site‐selective protein dual modification is based on the differences in the reactivity of cysteines in their free (thiol) and oxidized (disulfide) forms.3.2Despite the simplicity of dual modification at two distinct sites, the availability of proteins with two different AA residues with orthogonal reactivities is rather limited. Therefore, alternative methods have been developed to target one specific AA residue with a multifunctional bioconjugation reagent.4, was required to retard the elimination reaction to generate a stable and bioactive conjugate for subsequent applications.Baker, Caddick, and co‐workers have demonstrated protein dual modification using mono‐ and dibromomaleimide reagents.Alternatively, vinyl sulfones are also commonly explored Michael acceptors for protein modification due to its high electrophilic properties that enable their reaction with nucleophiles on the protein surface.In addition to maleimide analogues and sulfone derivatives, other strategies have also been developed for dual‐modification. For example, Goncalves and co‐workers reported that dichloro‐1,2,4,5‐tetrazine can undergo two successive nucleophilic aromatic substitutions to introduce the thiol‐containing payload at a cysteine residue with excellent selectivity Figure .[44] TDue to the emergence of ADCs for targeted cancer therapy, dual functionalization at disulfide site has also gained growing interest because of the presence of accessible disulfide bonds in antibodies and the antigen‐binding fragment (Fab).11, 12Besides addressing cysteines and disulfide bonds, other modification strategies at less‐explored AA residues have also been reported to expand the existing protein functionalization toolkit. For example, the hypervalent iodine reagents developed by Gaunt and co‐workers can be combined with maleimide reagents to achieve dual functionalization at cysteine and methionine sites, which is described in Figure Achieving dual modification at one single AA site has less restriction in terms of the choice of reagents compared to the functionalization at two AA sites. One possible limitation of the single site strategy is that the two orthogonal groups can be sterically hindered due to close proximity on a relatively small multifunctional linker. This could prevent bulkier groups such as polymers to be attached onto the protein. In addition, modification besides cysteine residues is relatively unexplored, thus this field would greatly benefit from further investigations of modification strategies at other low abundant AA residues such as tyrosine, serine or the N‐termininus.4To expand the reactivity beyond what is offered in native proteins, reactive groups for functionalization through genetic incorporation of new AAs  has emerged as an indispensable tool for site‐selective protein dual functionalization. In this section, genetic methods to integrate new reactive canonical AAs, ncAAs or peptides tags for protein dual functionalization are summarized.4.1The expression of recombinant proteins containing one or more point mutations is a straightforward and well‐established technology.The examples mentioned above took advantage of the different local microenvironment offered by the native protein. Inspired by Nature's elegance, Pentelute and co‐workers have developed a strategy to create a specific local chemical environment for the cysteine residue by a newly developed, fine‐tuned four‐amino‐acid peptide sequence (FCPF), which is termed as “π‐clamp” Figure .[96] T4.2E. coli are applied that are not able to synthesize a certain amino acid required for its growth. The ncAAs need to structurally resemble the natural AA to allow binding to the respective endogenous aminoacyl‐tRNA synthetase and to replace the natural AA in the polypeptide sequence.E. coli strain.The introduction of point mutations has certain limitations as the available functionalities and their respective reactivities could only be selected from the pool of the 20 canonical AAs. However, the cellular biosynthetic machinery can be manipulated to incorporate ncAAs that often represent structurally similar derivatives of the canonical AAs. These ncAAs allow protein labeling with unprecedented molecular precision.CUA pair (MjTyrRS/tRNACUA pair) derived from Methanococcus jannaschi in response to the amber (UAG) stop codon to insert p‐acetylphenylalanine, a ketone bearing ncAA, into T4 lysozyme.The genetic code expansion technique has been developed as another technique that allows the insertion of a broad variety of ncAAs with spatial precision at virtually any desired site.CUA pair (PylRS/tRNACUA pair) to produce a new variant, PyIRS/tRNAUUA, which can suppress the ochre (UAA) stop codon. In combination with the evolved MjTyrRS/tRNACUA pair, these two orthogonal aaRS/tRNA pairs were capable of recognizing and inserting two ncAAs, p‐azido‐l‐phenylalanine and Nϵ‐propargyloxycarbonyl‐l‐lysine, into a glutamine‐binding protein in response to the amber UAG codon and the ochre UAA codon /tRNACUA pair and PyIRS (AcKRS)/tRNAUUA pair, to eliminate protein aggregation and oxidation induced by the copper catalyst.E. coli, Schultz and co‐workers have also successfully incorporated two different ncAAs, which contained azido and ketone groups, in mammalian cells by utilizing the two orthogonal Methanosarcina barkeri pyrrolysyl‐tRNA synthetase (MbPylRS)/Methanosarcina mazei pyrrolysyl tRNA (MbPylRS/MmtRNAUUA pair) and tyrosyl‐tRNA synthetase (EcTyrRS)/tRNACUA pair (EcTyrRS/tRNACUA pair).Despite the simplicity of introducing a cysteine mutation as one of the target sites for protein dual modification, this approach could become problematic if the protein consists of native cysteines that play important structural or functional roles. Furthermore, cysteine residues could form disulfide bonds, which could limit expression yields and the reaction reversibility of the thiol–maleimide conjugation could also cause stability problems of the resulting protein bioconjugates.n Figure .[100] UCCU (a derivative of MjTyrRS/tRNA) and PylRS/tRNACUA, two ncAAs were site‐selectively introduced into Calmodulin, forming a triazole intramolecular crosslink through the subsequent CuAAC reaction.UCCU pair in directing the corresponding ncAA incorporation. The efficiency of the original system was substantially improved based on the evolution of the PylRS/tRNACUA to obtain an optimized quadruplet decoding variant, PylRS/tRNAUACU pair.UACU pair was combined with the AzPheRS*/tRNACUA pair to site‐selectively introduce two bioorthogonal tags, norbornene and tetrazine, into Calmodulin  and a stop codon for the incorporation of two ncAAs.n Figure .[25] DGenetic code expansion has witnessed incredible achievements in recent years, which greatly promotes and revolutionizes the field of site‐selective protein dual functionalization. The advantages of this strategy lie in the small size of the ncAAs, flexible incorporation sites but high site‐selectivity, and the diverse reactive tags available for incorporation. However, some important challenges still remain in this rapidly evolving field, for example, low catalytic efficiency of engineered aaRS which require tedious evaluation and optimization to improve their performance, relative low expression yields, repetitive optimization of the protocols for effective protein engineering, the scope and compatibilities of the functional groups capable to be inserted.4.3Besides the incorporation of ncAAs bearing bioorthogonal tags for dual modification of proteins, the insertion of two artificial peptide sequences that can be recognized by two distinct enzymes for the subsequent covalent labeling with user‐defined probes also serves as a straightforward approach for dual modification of proteins. This enzyme‐mediated peptide labeling combines the advantage of high specificity of the enzyme towards the peptide tags and excellent labeling efficiency with minimal perturbation to proteins structure and function.Despite the simplicity of a one‐step introduction of the desired functionalities by two independent reactions, the labeling efficiency may be greatly influenced by the steric effects and hydrophobicity of the introduced probes. This limitation can be overcome by the incorporation of a multifunctional scaffold possessing the bioorthogonal tags. Distefano et al. used farnesyl transferase, which can catalyze the transfer of an isoprenoid group from farnesyl diphosphate to the cysteine site of a tetrapeptide , to introduce two reactive tags to the model protein, GFP Figure .[107] In some instances, the enzyme‐mediated dual labeling of proteins can also be achieved on the native protein surface in a site‐selective manner without the need to insert artificial peptide tags. For example, in Alabi and cowokers’ work, microbial transglutaminase, which catalyzes the formation of interprotein isopeptide bonds between glutamine and lysine residues, can recognize the glutamine 295 on the aglycosylated human IgGs and thus allowed the incorporation of two bioorthogonal tags, azido group and methyltetrazine group Figure .[80] T5Driven by exciting developments in the discovery of new synthetic procedures as well as scientific curiosity, achieving higher level of protein modification in a site‐selective manner is considered crucial for advancing different fields in chemistry, biology and material science. Compared to mono‐ and dual functionalization of proteins, triple functionalization strategies allow the incorporation of three different functionalities into proteins, thus offering well‐defined protein bioconjugates with further expanded structural and functional diversities that is beyond what current synthetic strategies can accomplish. These endeavors will greatly boost our capability to investigate, modulate and re‐design the chemical and physical properties of proteins.However, triple functionalization of proteins is even more demanding than mono‐ and dual functionalization as the combination of three orthogonal chemistries have to be applied in aqueous solution and in the presence of many reactive proteinogenic groups. Furthermore, by increasing the number of functionalities attached to the protein surface, it may lead to potential detrimental influence to their functional and structural integrity. In addition, the selection of the reactive groups requires more deliberate chemical design as the increase in the hydrophobicity of the bioconjugation reagents may cause protein aggregation. The labeling efficiency may also be compromised due to the steric hindrance from the insertion of three different functionalities.MjTyr, and Pyl pairs to suppress UGA, UAG and UAA codons for the incorporation of three ncAAs, 5‐hydroxytryptophan, p‐azidophenylalanine, and cyclopropene‐lysine, in the engineered E. coli strain ATMW1 carbonyl)‐l‐lysine, 3‐methyl‐l‐histidine and Nϵ‐(carbobenzyloxy)‐l‐lysine, were genetically encoded into proteins in response to the UAG, AGGA and AGUA codons. However, subsequent functionalizations have not demonstrated yet. Compared to the reassignment of three stop codons, this work utilizes the combination of a stop codon and quadruplet codons, which do not interfere with the translation termination and serve as a more general and applicable strategy to achieve higher‐level protein functionalization. Even though there is no application shown and there are still obvious limitations, for example, low encoding efficiency, these examples represent the first proof‐of‐concept for protein triple modifications. They still represent significant technological advancements for the development of multifunctional protein conjugates, which holds immense potential for drug delivery, bioimaging, and material science.Recent examples have demonstrated the successful triple functionalization of proteins. Chatterjee and co‐workers have reported the introduction of three ncAAs into proteins via genetic code expansion. In this work, they assigned the EcTrp, 1 Figure .[119] Besides increasing the number of functionalities that can be incorporated into proteins, more complex protein bioconjugates have also been reported. For example, Chudasama et al. described an efficient and modular strategy for the generation of a bispecific antibodies as well as the dual functionalization of the resulting bioconjugate Figure .[122] 6With the emergence of protein dual functionalization techniques, homogeneous protein bioconjugates with remarkable functional complexity have been achieved and studied for diverse applications. In the following, three major fields of applications are highlighted that have greatly benefited from these novel synthesis opportunities.6.1The excitation energy transfer from a donor to an acceptor chromophore, also termed Förster resonance energy transfer (FRET), allows assessing important parameters such as chromophore distances and their orientations in real time and in complex biological environments. Although cryo‐electron microscopy is becoming a popular technique to determine structural snapshots of biomacromolecules at atomic resolution, samples need to be frozen and studies cannot be carried out in native environment. In contrast, FRET bears the advantage of easy accessibility and enables the real‐time monitoring of the dynamics and conformational changes of proteins in their native environment, thus serving as an important complementary methodology for elucidating proteins structural dynamics.2A receptor in a site‐specific fashion, which, in conjunction with a CFP, generated a FRET construct (A2A‐FlAsH‐CFP) that exhibited a 5‐fold enhanced agonist‐triggered FRET signal compared to the dual‐tagged CFP and YFP conjugate , for example, cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP), can be genetically encoded into the target proteins and are widely utilized for the investigation of proteins inside living systems.e Figure .[127]2+) concentration. CaM consists of two domains, the C‐terminal and N‐terminal domain that contain two EF‐hand motifs to bind Ca2+.2+binding, the conformation of CaM changes, which affects its binding to different target enzymes.In comparison to FPs, small molecule chromophores bear the advantage of small size, excellent photostability and high quantum yield.2+ and without Ca2+ have been resolved in detail via NMR or X‐ray crystallography. However, the transition states that dynamically occur during the Ca2+ binding processes and that play an important role in modulating the interaction of CaM with its binding partners could not be resolved with conventional structure analysis by NMR and X‐Ray.2+ concentration  but with different configuration  was conjugated with the three different drug linkers via thiol–maleimide chemistry offering three different ADCs, cAC10‐4 (without PEG24), cAC10‐5 (with PEG24) and cAC10‐6 (with branched PEG24) as depicted in Figure Despite the clinical success, most ADCs are still restricted to the conjugation with a single type of payload, for example a certain anticancer drug, to target a specific type of cancer cells. In fact, this kind of single‐functionalized ADCs often suffer from the unfavorable pharmacokinetics and the inherent hydrophobicity of the chemotherapeutic drugs, which could significantly limit the in vivo therapeutic efficiency.n Figure .[132] On the other hand, the therapeutic efficacy ADCs could also suffer from drug resistance after multiple treatments due to inherent and acquired resistance or tumor heterogenicity.89Zr for theranostic applications  to anti‐prostate stem cell antigen (PSCA) cysteine mutated diabody A2 via a dual‐modality linker, offering the dual‐modified imaging probe (18F‐ sCy5‐A2cDb)  Figure .[138]n Figure .[138] n Figure . The dua7The technical breakthroughs in protein bioconjugation chemistry has served as a major driving force for the elucidation of protein trafficking and interactions in living cells as well as the emergence of protein therapeutics. With the increasing need for personalized treatments to address great challenges in biomedical research such as efficient cell or organ targeting, overcoming drug resistance and reducing systemic toxicity, there is a high demand for protein therapeutics with improved features. Multifunctional protein conjugates provide more than one functionality, and they are expected to enhance in vivo performance of therapeutic proteins by combining different functional groups possessing therapy, diagnostics and imaging properties in a single protein system that is tailored to the requirements of the patient. Nevertheless, dual functionalization of proteins remains much more challenging relative to single functionalization owing to the plethora of reactive functional groups on the protein surface and the requirement for the optimal combination of different orthogonal reactions with high efficiencies. In this review, we have summarized the remarkable progress in both synthetic and genetic engineering strategies that have overcome these significant hurdles and thus allow covalent functionalization of proteins with two different functionalities at distinct sites.Undoubtedly, dual functionalization of proteins has witnessed significant advancements over the past ten years which offers a new arsenal of functional protein conjugates with advantages over singly‐functionalized proteins in probing protein dynamics, combination therapy and bioimaging. We envision that tremendous efforts will continue to be devoted to enriching the current methodology toolkit by exploring new chemistries to improve the specificity as well as efficiency of dual functionalization or to achieve a higher level of functionalization, for example, triple functionalization. Moreover, the proper combination of different sophisticated strategies, for example, chemical methods, genetic methods, to capitalize the advantage of different approaches from the currently expanding toolbox will provide new insights for the preparation of a myriad of functional nanomaterials. Ultimately, we believe that this will pave the way towards “smart” and “intelligent” protein conjugates that can self‐adapt to the microenvironment of diseases and provide a self‐feedback loop to achieve an output or decision, for example, termination/activation. In this way, current bottlenecks and challenges in therapeutic applications can be addressed with a completely new perspective through rationale chemical design.The authors declare no conflict of interest.Lujuan Xu studied chemistry at Zhengzhou University, where she received her Bachelor's (2014) and Master's (2016) degrees. From 2014 to 2016, she conducted her Master's thesis on the synthesis of small organic molecules for optoelectronic devices in the group of Prof. Jian Pei at Peking University. In 2017 she received a CSC scholarship to undertake her PhD with Tanja Weil at the MPIP. Her research focuses on the development of new chemical methodologies for site‐selective modification of proteins.Seah Ling Kuan received her B.Sc (Hons) in 2003 and completed her PhD (Chemistry) at the NUS in 2009. She joined Prof. Tanja Weil in NUS from 2008–2010 as a research fellow. In 2011, she began her independent research as an Alexander von Humboldt fellow in UUlm. She was a group leader in the Institute of Organic Chemistry III at UUlm from 2013–2016. She joined the MPIP in 2016 as a group leader (Protein Therapeutics). Her research focuses on the development of chemical approaches for the innovation of precision protein therapeutics to address devastating diseases such as cancer, inflammation or infections.Tanja Weil received her PhD from Mainz University in 2002. She held several leading positions at Merz Pharmaceuticals GmbH . She was an Associate Professor at the National University of Singapore  and since 2010, director at the Institute of Organic Chemistry III (OC III) at Ulm University (Uulm). In 2016, she was appointed as director of the Department of Synthesis of Macromolecules at the Max Planck Institute for Polymer Research (MPIP) in Mainz, Germany. She has received an ERC Synergy Grant. Her scientific interests focus on innovative synthesis concepts to achieve functional macromolecules, hybrid materials and life‐like systems to solve current challenges in biomedicine and material science.


With the rapid increase of investigations in biomolecular LLPS and growing popularity of LLPSDB, we updated the database, and developed a new version LLPSDB v2.0. In comparison of the previously released version, more than double contents of data are curated, and a new class ‘Ambiguous system’ is added. In addition, the web interface is improved, such as that users can search the database by selecting option ‘phase separation status’ alone or combined with other options. We anticipate that this updated database will serve as a more comprehensive and helpful resource for users.Emerging evidences have suggested that liquid–liquid phase separation (LLPS) of proteins plays a vital role both in a wide range of biological processes and in related diseases. Whether a protein undergoes phase separation not only is determined by the chemical and physical properties of biomolecule themselves, but also is regulated by environmental conditions such as temperature, ionic strength, pH, as well as volume excluded by other macromolecules. A web accessible database LLPSDB was developed recently by our group, in which all the proteins involved in LLPS http://bio-comp.org.cn/llpsdbv2.LLPSDB v2.0 is freely available at: Bioinformatics online. Biomolecules, mostly including proteins and nucleic acids can form liquid-like membraneless condensates, such as membraneless organelles (MLOs), via liquid–liquid phase separation (LLPS) . The LLPhttp://bio-comp.org.cn/llpsdb) is the only released database designed specifically for proteins undergoing LLPS that have been validated by experiments in vitro , not based on experimental conditions , same as LLPSDB. Those entries in which the components in the experimental systems are clear classified as ‘Unambiguous system’, and the rules of data collection, curation and management for this class are consistent with the previous version. A new classification named as ‘Ambiguous system’ is identified when an experimental system contains unclear (or unconfirmed) proteins , or part or all ingredients (proteins and/or RNA/DNA) are integrated as one component in the LLPS experiment   such as localization, Gene Ontology (GO) term and sequence (if not provided by literature) in LLPSDB v2.0 were obtained from UniProt/NCBI, while the rest of annotations were mining and curated from other related databases. The linkages of the related databases [such as DisProt , 30 from prokaryote and 16 from virus (a new emerging group of proteins in LLPSDB v2.0) . Naturalin vitro, most of the investigated systems  have LLPS evidences in vivo/in cell at the meantime.For the total 6678 experimental conditions in LLPSDB v2.0, 4271 of them belong to phase separated status, 1959 of them belong to ‘no LLPS’ status and 448 of them correspond to phase diagrams. The numbers of all the three subsets are 2–3 times as large as those in the previous version LLPSDB. Although we only collected the data that proteins undergoing LLPS The components in each entry in LLPSDB v2.0 may be specific protein(s), protein(s) and RNA/DNA or obscured complex. Therefore, the entries are grouped into ‘Unambiguous system’ or ‘Ambiguous system’. In ‘Unambiguous system’ group, similar as LLPSDB, the 2917 entries can be categorized into ‘Protein(s)’ (1945), ‘Protein(s) + RNA’ (617) and ‘Protein(s) + DNA’ (259) according to specific components, or can be categorized into ‘One component’ (1230), ‘Two components’ (1302) and ‘more components’ (289) according to component number. For the total deposited 96 entries in ‘Ambiguous system’, which is a group not included in LLPSDB, 56 of them contain protein(s) with clear information, and the other 40 contain only complex (such as nucleosome).Home, Browse, Search, Submit, Statistics, Download and Help modules as LLPSDB. The interactive interface of ‘Browse’, ‘Search’ and ‘Download’ modules are ameliorated for users’ convenience.LLPSDB v2.0 allows users to browse, search, submit and download the data, and keeps Browse module, the curated data are grouped into ‘Unambiguous system’ and ‘Ambiguous system’. For ‘Unambiguous system’, same as LLPSDB, three classifications can be browsed based on: protein type, main components type and main components number. The detailed information of independent protein (named as ‘Protein Details page’ in LLPSDB) can be browsed by ‘protein type’ , through clicking on the name of specific protein on ‘Protein List page’ displaying on the right side. A brief table of entries that this protein involved in (named as ‘Table of Entries’) is presented at the bottom part on ‘Protein Details page’. The ‘Table of Entries’ displays the brief information of main components in each entry, and can be browsed directly for the other two classifications (‘main components type’ and ‘main components number’). More detailed information of each entry (named as ‘Entry page’) is available through clicking on the Entry ID in ‘Table of Entries’, which includes ‘General information’ as well as ‘Phase separation conditions’ of experimental system. For ‘Ambiguous system’, a brief table which includes ‘Component name’, ‘Brief description’ of the system and ‘Nucleic acid’ is presented, and the detailed ‘Entry page’ is accessible through Entry ID , it is presented in a manner similar as the ‘Phase separation conditions’ part in ‘Entry page’ when the search option ‘phase separation status’ is selected in LLPSDB v2.0. The bottom window of In  options . At thisDownload page. The data for download are grouped into ‘phase separation’, ‘no phase separation’ and ‘phase diagram’ based on ‘phase separation status’ for ‘Unambiguous system’ and ‘Ambiguous system’ respectively. Users can download the classified datasets according to their own needs.All datasets are free available on the Help page.Detailed guidance on the usage of LLPSDB v2.0 can be found on the http://bio-comp.org.cn/llpsdbv2) has been made a significant improvement both in the data quantity and in web service interface compared with previous version LLPSDB. It is more than twice the data volume of LLPSDB. A new class ‘Ambiguous system’ is curated in which part or all components in experimental system are unclear. Totally, LLPSDB v2.0 contained 2917 experimentally validated entries including 586 independent proteins and 6678 corresponding experimental conditions (included 448 phase diagrams). Meanwhile, to provide a more user-friendly interface, we implemented the online service of LLPSDB v2.0 by PHP, improved the Search module by adding more options and provided selectable download service. We believe that the updated database provides a more useful resource for better understanding the mechanism of protein LLPS.In summary, LLPSDB v2.0 (in vivo, etc. Furthermore, we are actively using our database to build prediction tools, which may be provided by online service for users in the near future.To make LLPSDB as an ever-growing comprehensive database, we will continuously update and expand our database by collecting the latest data. Firstly, we may collect and curate more types of liquid-liquid separation systems in future versions, such as those only containing lipids, RNA or DNA and those undergoing LLPS with quantitatively measured information btac026_Supplementary_DataClick here for additional data file.


Advances in next generation sequencing technologies provide wealth of data on genome variations. Understanding missense mutations is crucial to tackling global health problems related to inherited diseases and the emergence of drug resistance in cancers and infectious diseases. Advancement in research at systems and molecular level, is required to study the impact of mutations that affect both the regulation of gene expression and protein function through changes in protein stability and affinity towards other proteins, nucleic acids, biomolecules and small molecule ligands. High-quality experimental data on protein structure, mutant stability, functional annotations and phenotype-genotype associations in combination with the state of art techniques in artificial intelligence and machine will revolutionise development of highly accurate predictive computational models to study the impact of genetic mutations on human health and disease.Predictive computational models offer an effective alternative to expensive experimental studies of genetic variations. These models can identify potential mutations linked to disease conditions and the emergence of antimicrobial drug resistance. At the molecular level proteins, via their interactions with other proteins and biomolecules, play an important role in many biological processes. The growing data on protein three-dimensional structure, along with variations observed in sequence data, will enable the development of new computational methods and tools to predict the impact of mutations on protein function, stability and interaction thereby aiding in the understanding of the basic mechanisms that govern disease conditions.Turina et al. developed a semi-automatic text-mining tool to extract protein mutant thermostability data from the scientific literature. Feng et al. studied the role of phosphatase and tensin (PTEN) homolog gene mutation in low grade gliomas progression and prognosis. Using patient’s RNA sequencing data, differential gene expression and gene ontology analysis they showed that PTEN mutation promote tumorigenesis and immune cell infiltration. Tan et al. trained a predictor using saturation mutagenesis data to access the impact of point mutations on protein stability and function. Mutants are scored using a statistical potential energy function derived from protein structural data in combination with evolutionary sequence conservation and substitution scores. Using the physicochemical properties of amino acids Savojardo et al. grouped variants linked to human genetic diseases into four types and established mapping between mutations, diseases, and phenotypes through the protein family domains. Tunstall et al. designed an in silico framework to understand pyrazinamide resistance mutation in the clinical isolates of four main M. tuberculosis lineages. Using a combination of genomic features and computational mutant stability and drug affinity predictors to explain differences in modern and ancient lineages within the context of drug resistance. Using molecular dynamic simulations, Nangraj et al. investigated the mutation in pncA gene of Mycobacterium tuberculosis that confer resistance to the first line drug pyrazinamide and explained resistance in term of the changes in protein stability and drug binding site. Prabantu et al. modelled protein structures as network where nodes and the edges correspond to residues and interaction between residues respectively. They showed that the differences between wildtype and disease mutants can be explained by their respective changes in the network both locally at the site of mutation and globally that relate to protein allosteric effects. Birolo et al. analysed both pathogenic and benign variants in haploinsufficient genes and reported that variants significantly perturbing stability (both the stabilising and destabilising) correlate with pathogenicity. Mahlich et al. performed mutational analysis using variant effect predictor on human proteins and its orthologous from 20 species. They analysed the impact of common and rare variants in terms of conservation and also suggested that cross-species variants (CSVs) might be more often neutral than non-CSVs. Bhasin and Varadarajan used large scale mutational scanning dataset to study the mutational sensitivity and substitution preferences at buried and exposed positions. They used mutational sensitivity data and predicted sequence-based accessibility values to identify buried, active-site and exposed non active-site residues. Soto-Ospina et al. aimed to understand the impact of pathogenic mutations in amyloid precursor protein Presenilin 1 that are known to cause Alzheimer’s disease. They used molecular modelling and dynamic simulations to explain the impact of mutations in terms of structural modifications of active site mutant residues found at the catalytic pore. In a focused review, Grace et al. explored the use of molecular docking and dynamics to study resistance mutation in Mycobacterium tuberculosis within the context of anti-tuberculosis drugs.This research topic highlights the recent developments in computational approaches to analysis and predict the impact of mutation on protein stability, function and interaction. Development of accurate protein mutant stability requires the availability of properly curated high quality experimental thermodynamic dataset. To facilitate this, Understanding the impact of genetic mutation is critical to tackle disease and drug resistance. Experimental structures of biomolecules are becoming available at a rapid pace due to the recent developments in the field of cryo electron microscopy. In parallel, the technology development in computing hardware and software has enabled development of robust machine learning models to predict the structure of proteins and its interactions. Both these recent developments complement each other to provide high quality structural data of biological macromolecules and small molecules including drugs. The timing of these recent developments will enable decoding the complex mutational landscape and enable our understanding of the genotype to phenotype relationship, paving way to the achievement of precision medicine.


The field of structural proteomics, which is focused on studying the structure–function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storing increasing amounts of protein structural data, in addition to modeled structures becoming increasingly available. This, combined with the recent advances in graph-based machine-learning models, enables the use of protein structural data in predictive models, with the goal of creating tools that will advance our understanding of protein function. Similar to using graph learning tools to molecular graphs, which currently undergo rapid development, there is also an increasing trend in using graph learning approaches on protein structures. In this short review paper, we survey studies that use graph learning techniques on proteins, and examine their successes and shortcomings, while also discussing future directions. Proteins are the building blocks of all cells in our bodies. Although the DNA molecule holds all the information that is necessary for life, it is proteins that carry out what is coded in the genetic material . As protgeometric deep learning was created, encompassing these techniques [Parallel to the increase in structural data in the field of biology, novel machine learning (ML) and deep learning (DL) approaches are being developed that can harness huge amounts of data to achieve high predictive performance ,12. In tchniques , a subsechniques . Graph-bchniques . Graph lchniques  and advachniques .Given the increase in protein structural data and the success of graph learning methods, it is natural for studies that are employing graph learning models in the structural proteomics field to emerge. The goals of this short review are to:Provide related work on protein graph-based representations.Introduce the graph representation learning (GRL) field, and explore its potential use to structural proteomics.Report studies in six different proteomics task categories where graph learning models have been successfully used.The graph representation of a protein structure collapses its 3D conformation into a graph, where now, the geometric information is incorporated within the graph connectivity, and not explicitly encoded in a coordinate system. Nodes in the graph can be defined at an amino acid level, where each node corresponds to a different amino acid in the protein sequence. Biochemical features for each node include can polarity, charge, hydrophobicity and others . ProteinHowever, transforming a protein tertiary structure to a graph is far from trivial, as different topological and geometric information can be taken into account in the process. As such, different geometrical/biological methods arise for creating the graph. A simple way is to calculate all residue pairs distances, and introduce an edge between two residues if the distance is smaller than an empirically proposed cutoff manifold . Many stmanifold ,19. Suchmanifold   serves as a blueprint to design and create GNN models. There are many GNN variations ,35, instation hi . For exai, GCN’s  AGGREGAT network . Hence, rk (GAT) , which i serves as paper (,35 proviProtein topology has a central role in many biological processes. Therefore, GNNs, and the GRL field as a whole, being able to harness topological information, can potentially be a great tool in advancing the structural proteomics field. In the next few sections, we will examine GRL approaches that have been used for six proteomics-related tasks Table .Protein–ligand interaction has been a well-studied problem. The underlying physicochemical mechanisms, i.e., binding kinetics, basic thermodynamic concepts, and the binding driving forces/factors have been extensively studied . HoweverStudies have shown good drug-target affinity predictions when training a GNN on a sufficient amount of protein–ligand complex data Figure , and oneGRL methods have been used even when protein–ligand complex data are not available. The corresponding studies treat the ligand and the protein as separate entities, with ligands being represented as small atom graphs, and proteins as large residue or atom-based graphs Figure . The autAs the number of datasets and studies that GNN-based methods are tested is still low, it is unknown how well the methods discussed above generalize. Moreover, in the case where the protein–ligand complex is not given Figure , there iIn a protein interaction, it is not only valuable to know the binding affinity, but also, the components that determine the interaction, i.e. the binding sites. In one of the first studies to use GRL approaches to structural proteomics, a protein–protein interaction system based on multiple stacked layers of graph convolutions was developed . Given aThe 3D conformation of a receptor–ligand complex can affect specific biologically related functions, such as driving the cellular immune response . To thisOverall, GNNs seem to show promise in regards to scoring conformations. However, it is questionable how well receptor–ligand graphs with microscopic structural differences can be differentiated with a GNN for scoring. Additionally, no extensive tests have been performed with ML-based scoring functions that use more fine-grained structural features than the whole receptor–ligand graph .Predicting the 3D structure of a protein given its sequence is one of the most challenging problems in the field of computational biology . DespiteIn line with graph-based methods exploding in popularity, GNN-based approaches have also been tested in QA, with GraphQA , based o VoroCNN  builds a VoroCNN  is a difThe abundance of different graph methods, as well as the increased performance gains in comparison to other methods, indicate that GNN-based architectures are well suited to the QA task. As the methods presented above employ different graph convolution architectures and graph representations, future work includes benchmarking those methods on different datasets to further investigate which graph representations and architectures work best.Protein function prediction is a challenging task that is approached by various sequence-based and structural-based methods . HoweverDespite the good results obtained so far, it is still not clear if the graph representation of a structure can aid in predicting protein function. In particular, the study in  shows thStructured Transformer introduced in [Protein design seeks to support the manual engineering of proteins for specific functions, without relying on evolutionary mechanisms . More foduced in , inspireGNNs have also shown potential in aiding protein design in other ways. In , a variaThis paper examines the potential of GRL to incorporate protein structures in various biological problems. The increasing availability of structural datasets and the recent explosion of work in the GRL field have increased the potential of exploiting structural priors in downstream proteomics tasks. Such structural information could be the primary reason why graph-based models edge out their sequence-based counterparts in regards to the studies discussed.The GRL field was introduced essentially in the last 5 years, and, ever since then, it has made a huge impact on multiple scientific fields. Given the preliminary results from proteomics-related studies that employ GRL, its use to bioinformatics and computational biology is expected to inspire much structural proteomics research in upcoming years.The increased availability of protein structural data has enabled the use of graph deep learning approaches in structural proteomics.GRL approaches have already been successfully employed in a multitude of tasks in many different domains/fields.In structural proteomics, GRL approaches have been used in protein–ligand interaction, protein function prediction and protein design, among others.Results from the studies discussed indicate that there is potential in using GRL methods together with ever-increasing protein structure data for a multitude of structural proteomics tasks.


The different experimentaland computational methods used to block this interaction provide aroad map for the future of PPI engineering.Theformation of specific protein–protein interactions (PPIs)drive most biological processes. Malfunction of such interactionsis the molecular driver of many diseases. Our ability to engineerexisting PPIs or create new ones has become a vital research tool.In addition, engineered proteins with new or altered interactionsare among the most critical drugs that have been developed in recentyears. These include antibodies, cytokines, inhibitors, and others.Here, we provide a perspective on the current status of the methodsused to engineer new or altered PPIs. The emergence of the COVID-19pandemic, which resulted in a worldwide quest to develop specificPPI inhibitors as drugs, provided an up-to-date and state-of-the-artstatus report on the methodologies for engineering PPIs targetingthe interaction of the viral spike protein with its cellular target,ACE2. Multiple, very high affinity binders were generated within afew months using  The formation of specific interactionsbetween proteins within the crowded milieu of the living organismis crucial for all aspects of life. Proteins interact with other proteinsto form signaling networks, to drive the immune response, to controltranscription and translation, to regulate enzyme activity, and muchmore. Due to their large, heterogeneous surfaces, protein interactionscan bind quickly, tightly, and specificaly to their partners, evenin environments with a large number of competing noncognate molecules.This happens at an incredible range of concentrations, from millimolarto femtomolar. With protein–protein interactions (PPIs) beingparamount in all aspects of life, it is not surprising that theirmalfunction is a driver of many diseases. At the same time, they havebecome a major source for drug development. Proteins forming specificinteractions to modify biological processes are now the hottest sellingnew drugs globally. Five of the ten top-selling drugs  arebiologicals , which act by forming specificprotein interactions. This is a result of the massive progress inprotein engineering that has been achieved during the past 40 yearswhen protein engineering was at its humble start.3 From the earlydays, it was envisioned that the ability to design protein moleculeswould open a path to the fabrication of devices of complex atomicspecifications. Engineering existing PPIs for higher affinity or creatingnew interactions was between the first applications of protein engineering.Nature invented protein engineering hundreds of millions of yearsago, with the development of small antibodies. Kohler and Milstein4 applied the technology of antibody engineeringfor the production of mouse monoclonal antibodies by hybridoma technologyand by this opened the door to engineer binders by need. However,they let nature make the selection, as understanding protein structure–functionrelations was still in its infancy.Protein engineering started withthe redesign of proteins to understandenzyme mechanisms, protein structure, and folding.7 The overall architecture of protein–protein binding siteswas suggested to include a hydrophobic, water-shielded interface core,surrounded by polar residues that provide specificity. However, differentproteins show very different modes of interaction. Thus, the use ofrules here is much more limited than for protein folding, where onealways finds a hydrophobic core and polar surface (which may be whythe design of de novo proteins seems to be a morestraightforward task).A fundamental requirementfor designing new or enhanced protein–proteininteractions is understanding the nature of protein–proteininterfaces. Natural protein–protein interfaces show a complementarityof only 70–75% between the surfaces of the partners, with therest being occupied by water molecules within the interface.8 which provide information about the deletionof a specific amino acid  but not about the effectof substituting one amino acid with another one. This approach wasrevolutionized by the rise of so-called deep mutational scanning.9 In an elegant example by Heyne,10 they combined protein randomization, yeast surface display,deep sequencing, and few experimentally measured KD data points. This resulted in the generation of bindingdata for all possible mutations within the interface between two proteins,BPTI and bovine trypsin. This kind of data is a gold mine for tuningforce fields for PPI design, a task that current algorithms fail toaccurately predict.13One of the troubling aspects hamperingour efforts to tailor PPIsto our needs is the lack of knowledge about the effects of individualmutations on binding. Most studies applied alanine scanning mutagenesis,14 Moreover, using random mutagenesishas shown that most proteins can form high-affinity PPIs with manyother partners by introducing a small number of mutations.17 These findings clearly show that PPIs are not unique. Moreover,as the gap between protein thermostability and its working conditionsincreases, it allows for introduction of more mutations with destabilizingproperties yet generating new PPIs without negatively affecting itsstructural integrity.18 Thus, prestabilization of a proteinmakes it easier for engineering, including PPIs.When engineering a protein interface, we shouldfirst ask ourselveshow unique its composition is. Previously, we examined the plasticityof the interface of TEM1-β-lactamase with its protein inhibitorBLIP and showed that most interfacial residues could be mutated withouta loss of binding affinity, protein stability, or enzymatic activity,suggesting plasticity in the interface composition supporting high-affinitybinding.19 has shown that the effectof mutations on binding is additive within PPIs. This study was furtherrefined by a study showing that PPI interfaces are organized in amodular manner, with a module comprising several residues from bothbinding partners that form a continuous network of interactions. Theadditivity of mutation was found to hold for residues located in differentmodules, while within modules, there is significant cooperativitybetween residues.20 This led to a designprinciple in which complete interface modules were replaced, whichresulted in the design of the specificity of binding for similar interfaces.21 Along the same lines, binding specificity wasalso achieved together with high affinity by extending the interfaceto include a new specificity module.22 Furtherdevelopment in PPI specificity design was demonstrated by Netzer etal.,23 who aimed to design new high-specificitycolE-immunoprotein pairs on top of the known interaction between thesetwo proteins. Using a multistep design, they achieved pairwise specificityswitches of >3 orders of magnitude relative to at least one ofthenoncognate proteins. They suggest that preorganized backbone conformationswere more likely to result in high-specificity binding, providinga guideline for specificity design.Single-mutationchanges provide only a partial picture of the energeticswithin a protein–protein interface. An early, groundbreakingstudy by Wells24 Thus, knowing the identity of hot spots is agreat tool for disrupting existing PPIs by introducing very few mutations.However, this is not sufficient for engineering new or altered PPIsfor specificity or higher affinity, which has been a major goal ofprotein engineers from the beginning. For this, protein engineeringmethods have been developed along two main avenues: one is computational,and the second is experimental, creating mutation libraries and selectingthem for the desired trait. Next, we will shortly summarize progressin each of the two routes  offers a simple and efficient screeningprocess.25 Due to their success, yeastdisplay methodologies are constantly evolving.28 For example, a method was devised to select faster bindingproteins through pre-equilibrium selection.29 Here, the prey and bait are only transiently incubated before selection,giving an advantage to faster (rather than tighter) binding proteins.Further method development was introduced by creating N- and C-terminalfusions to proteins with enhanced stability and fluorescence, acceleratingthe method and allowing even tighter binders to be selected.15In 2018, the Nobel Prize in Chemistry was awarded for thedevelopmentof the phage display method for the 300 for a small 100-amino acid,300-nucleotide protein). This is obviously not feasible as the numbersurpasses the number of atoms in the universe (1078–1082). Yeast display libraries can include ≤109 variants, while phage and ribosome display libraries can be composedof ≤1012 different variants. However, the actualnumber of correctly screened colonies is smaller due to transformationefficiency and analysis errors and thus further restricts the maximumvariability of the library. This would allow for complete randomizationof only a few residues. Therefore, much effort went into the designof smaller, more focused libraries. For example, methods have beendeveloped to restrict either the positions or the amino acid mutations.For example, a library design with biased diversity in favor of Tyr/Ser/Glyresidues but with the addition of small quantities of other aminoacid types was sufficient to obtain many high-affinity antibodiesagainst numerous antigens.30 Another exampleis restricting positions and mutations to only partial randomization.31 Still, these options limit the library towardvariants of a very limited number of residues, which may not coverthe full potential to obtain binding. For this, all amino acids ofa protein should be mutated, as is done in natural evolution. Thisis usually done through error-prone polymerase chain reaction, whichcan be dialed to introduce two to four random mutations per protein.This would be sufficient to probe the complete protein with all possiblemutations. However, this is not probing the effect of mutations thatrequire two or three nucleotide changes to be reached (only 6–10other amino acids are reached by single-nucleotide changes) or epistaticmutations, where each mutation on its own has a negative effect. Toovercome this problem, one has to create multiple libraries on topof each other. A major problem here is that the intermediate species(single mutations) have reduced viability, due to either stabilityor lower affinity. Therefore, one has to probe the library througha path of least resistance and select many clones as the basis forthe next library. Using this strategy, we recently succeeded in obtainingpicomolar affinity binders of the receptor binding domain (RBD) ofSARS-CoV-2 spike protein binding to ACE2.32 In summary, while selection methods have proven to be highly successful,they suffer from a limit of selecting epistatic mutations when thereis high resistance of the intermediates33 , an algorithm based on artificial intelligence,learning from known protein structures, successfully predicted tohigh resolution the structures of a majority of the test proteins,a transformative achievement suggesting that we now can predict therelation between sequence and structure.36 Surprisingly, it seems to be easier to design a de novo protein or compute a protein structure than it is to design bindingsites from scratch or to predict binding sites. This was demonstratedby the results of the last critical assessment of predictions of interactions,37 where docking predictionsof the more difficult targets were problematic. Moreover, while computationaldocking is successful for the easier cases , the prediction of a protein networkpurely by multiple docking computations of all against all is currentlybeyond the computational limit.38 Amongothers, this is due to the good docking solutions also found for nonbinders,which suggests that current force fields are not sufficiently goodfor providing exact solutions. In line with the better success instructure prediction than in binding predictions, designing proteinsfrom scratch has by now become a doable task, with many examples given.39 One of the first successes in designing a newbinder is the computational design of a protein targeting the conservedstem region of influenza hemagglutinin by the Baker group.40 The design principle was first to identify hotspot residues making energetically favorable interactions with thetarget surface and then to configure a scaffold based on an existingprotein that anchors these energetically favorable interactions. Inthat case, the designed protein bound with very low affinity, whichwas enhanced by in vitro evolution using yeast displayto nanomolar affinity. This approach was later enhanced, by usinga large number of designed mini-proteins as a scaffold, reaching downto nanomolar affinity for influenza hemeagglutinin.41 An alternative approach for the rational design of de novo PPIs is the use of α-helices as the interfacesin de novo interactions. Here, one takes advantageof the well-known sequence–structure relationship of coiledcoils, and indeed, this method has shown great promise42  to theSARS-CoV virus that caused a pandemic in 2003, its primary mode offunction was well understood. Within weeks, the complete sequenceof SARS-CoV-2 was available, and structures of its main proteins appearedsoon after. With the first step of infection being the binding ofthe spike protein of SARS-CoV-2 to ACE2 in human airways, the inhibitionof this interaction became a prime target for drug development. Thisimmediately generated a race among many of the leading groups withexpertise in protein engineering to create such inhibitors. This providesus with an up-to-date and state-of-the-art reflection of the powerof current technologies.43 the RBM of ACE2 was used bymany groups either as a starting point to further enhance the affinityfor spike or using the RBM on ACE2 as a template to generate new spikebinding proteins. Also, the RBD of the spike protein was used as atemplate to enhance binding to ACE2, thus blocking the receptor frominteracting with SARS-CoV-2. While the methods mentioned above usedan existing naturally available template, other methods were template-free.Most prominent of these were the use of llama antibodies, called “nanobodies”,which are much smaller than human antibodies, more stable, and easierto use for binding selection, and DARPin molecules, which containnaturally occurring ankyrin repeat motifs that are used as a platformto rapidly evolve tight binders for therapeutic uses (Most groups targeted the spike proteinbinding motif (RBM) thatinteracts with ACE2, with few groups targeting ACE2 for blocking thisinteraction 2. With ttic uses 2. We sum44 While Chan etal. used in vitro evolution, Cohen-Dvashi et al.45 engineered a tight binding soluble ACE2 by computationaldesign. To identify preferred residues for mutation, they made useof the deep mutational scan of the ACE2 interface binding the RBD46 and used 70 orthologous ACE2 genes with highsequence identity to human ACE2. The stability and binding energytoward SARS-CoV-2 of selected residues were calculated using Rosettaatomistic modeling.47 Residues identifiedas giving an advantage were combined. The enhanced ACE2 variant boundRBD with an affinity of 30 pM versus 9 nM for the WT. This resultedin an ∼30-fold improved inhibition (IC50) againstviral entry. Glasgow et al. engineered a high-affinity ACE2 receptorusing a combination of computational design, followed by invitro evolution.48 First, theycomputationally designed the ACE2–RBD interface using Rosetta,including flexible protein backbone design, improving affinity by12-fold. This was followed by in vitro evolution,using random mutagenesis and yeast surface display, achieving an overall170-fold higher affinity compared to that of WT ACE2. After fusionto a human immunoglobulin crystallizable fragment to increase stabilityand avidity, they reached IC50s against viral entry oftens of nanograms per milliliter. Interestingly, while some mutationsselected on ACE2 were similar among these three studies, many otherswere not, despite achieving a much higher affinity in all cases, confirmingthe plasticity of PPI interfaces. Instead of improving ACE2–RBDaffinity, Guo et al. engineered WT ACE2 to form a trimer using a trimerizationmotif fused to ACE2.49 Trimerization increasedavidity, resulting in picomolar binding to the spike protein, despiteusing WT ACE2. The trimer showed high neutralization efficacy towardSARS-CoV-2.Chanet al. aimed to generate an ACE2 decoy with a very high affinityfor the RBD. For this, they created a library of all single mutationsat the ACE2 binding site, transfected them to Expi293F cells, andselected for the RBD of SARS-CoV-2. From the enrichment ratio, theychose the best binding mutants, which were combined and re-selectedfor epistasis. This resulted in a 40-fold improvement over that ofthe wild type (WT), which after dimerization (providing avidity) resultedin a potent ACE2 decoy.50 Creating a large number of designed peptidesand sorting them by yeast display for binding resulted in binderswith an ∼100 nM affinity. These were further optimized using in vitro evolution by yeast surface display, resulting in<100 pM affinity binders. These small inhibitors (∼60 aminoacids) express well and inhibit SARS-CoV-2 entry with an IC50 of 0.15 ng/mL. Linsky et al.51 used theknown binding site of ACE2 to design a small protein mimic. Still,as opposed to ref , resulted in a picomolar binding inhibitor.The inhibitor had an IC50 of 10–200 pM against thedifferent SARS-CoV-2 variants on VeroE6 cells and reduced the rateof SARS-CoV-2 infection in a hamster model.While most studies aimedto block the virus, Zahradnik et al. used the spike protein RBD todevelop an inhibitor against ACE2.50 within few months of the pandemic outbreakdemonstrates that engineering of high-affinity binders is now a realityand that multiple methods are available to the designer (45 other studies used computationas the first step, followed by in vitro evolution,while some studies relied on only in vitro evolution.This shows that in vitro evolution methods are verypowerful now, with new methods being devised all of the time, butthis also indicates that computational methods came a long way andare now reliable resources for the engineering of protein binders.While for achieving the highest affinity one still needs invitro evolution, the computational methods reduce the searchspace so that it can be managed by in vitro evolutionmethods.The success of generating many different SARS-CoV-2 inhibitorswith picomolar ICdesigner 2. No stu


Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles.To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model.In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein–protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.The online version contains supplementary material available at 10.1186/s12859-021-04323-0. The formation of various protein complexes is building blocks for nearly all physiological processes –5. The afunction , in whicfunction –14. Idenfunction –18. For function . The diffunction . Tthe diFortunately, nowadays a large variety of mature experimental techniques, such as surface plasma resonance (SPR)  and specDifferent from either type of above-mentioned computational methods, here we present a platform that combines both biophysics-based simulations with bioinformatics-based prediction to classify rates of protein–protein association presented in the SKEMPI database. A previously developed coarse-grained Monte-Carlo simulation Fig. a was usedc. We systematically tested ten different values of distance cutoff from 16 to 25 Å. For each value of dc, 103 simulation trajectories were carried out. The initial conformations in these trajectories are different from each other. After the initial conformation, as described in the “dc can be calculated for each complex in the benchmark set.We first applied kinetic Monte-Carlo (kMC) method to estimate the probability of association under different initial separation for each protein complex in a large-scale benchmark set. The detailed information about the construction of this benchmark and the algorithm of the kMC simulation can be found in the “3 simulation trajectories for 96 complexes under all 10 distance cutoff values, and failed to complete all the simulation runs for 6 complexes. As a result, these 6 systems were not considered in the following study. For the remaining 96 systems, the relation between dc and its corresponding association probability was used as the basis for further study, in which neural network model will be used to differentiate association rates between various protein complexes. The relations between different values of dc and association probabilities were selected and plotted in Fig. 8 M−1 s−1, the highest among these four systems. Similarly, the experimental association rate of 2VLR is the slowest (5 × 104 M−1 s−1).Among all the 102 protein complexes, we successfully generated all 10To generalize our study, we further tested the correlation between simulated association probabilities and their experimental measurements for all 96 protein complexes in the benchmark under different distance cutoff values Fig. . All assOn the other hand, we also noticed that these positive correlations are only moderate. More specifically, there are still a large number of outliers with high simulated association probabilities but low experimentally measured association rates, or with low simulated association probabilities but high experimentally measured association rates. However, it is worth mentioning that these association probabilities were derived from different distance cutoff. The association of a protein complex is a complicated process, and the pathways in the association of different protein complexes are case-dependent. It is possible that a protein complex with low experimental association rates but has high simulated association probabilities at certain distance cutoff, or vice versa. Instead of focusing on the association probabilities under single distance cutoff, it would be more informative to compare association probabilities from different distance cutoff values. As shown in Fig. In detail, a feedforward back-propagation algorithm was utilized to estimate whether the association rate of a protein complex is higher or lower than a predefined threshold. In specific, the input of the classification model is in ten dimensions, which are the association profile of a protein complex that was generated from kMC simulation with the distance cutoff values between 16 and 25 Å ), specificity (TN/(TN + FP)), precision (TP/(TP + FP)), and overall accuracy ((TP + TN)/(TP + TN + FP + FN)) were further calculated . Figure 5 M−1 s−1, corresponding to the logarithm value of 5.6 in the figure, the system achieves the optimal performance in which specificity, precision, sensitivity and accuracy all higher than 70%. Among all the 96 protein complexes in the benchmark set, there are 43 complexes which experimental association rates are lower than this optimal threshold and 53 complexes which experimental association rates are higher than the cutoff. Therefore, optimal performance was obtained when the testing set fall into two classes which sizes are relatively close to each other. Moreover, the neural network model can provide a confidence score (between 0 and 1) for each classification. Therefore, under the threshold of 4 × 105 M−1 s−1, we further broke down the accuracy of classification into different intervals of confidence score. As shown in Fig. https://github.com/wulab-github/KonPred.Figure 4 to 1 × 109 M−1 s−1 and monitored the correlated changes between TPR and FPR, leading into a collection of points as shown in Fig. A statistical analysis has further been carried out to test the classification results. In detail, after cross-validation was performed to all protein complexes in the benchmark for a given threshold, we also investigated the correlation between true positive rate (TPR) and false positive rate (FPR) from the classification results. The TPR is equivalent to sensitivity, based on the definition. The FPR, on the other hand, is defined as the ratio between the total number of FP versus the summation of FP and TN. Practically, both TPR and FPR are covariant with the choice of threshold. We thus changed the value of the threshold gradually from 1 × 10P values for all these tests are less than 0.0001. Therefore, the small P value for the t-test suggests that we can reject the null hypothesis and accept the alternative hypothesis, i.e., the differences between the outputs generated from our neural-network-based model and the outputs generated by random guessing are significant.In order to assess how significantly our obtained classification performance can be distinguished from random estimation, we carried out predictions with two different models. In one model, predictions were made by our neural-network-based classification method with the optimal threshold. In the control model, predictions were made purely by random guessing. Each protein complex was randomly assigned either higher or lower than the optimal threshold with equal probabilities. Predictions were carried out for all 96 protein complexes in the benchmark. This process was repeated 100 times for both models. After the predictions, the values of sensitivity, specificity, precision and accuracy were calculated, which distributions were plotted and compared in Additional file In summary, the statistical analysis on the cross-validation results demonstrated that we are able to identify protein complexes with high association rates from others with low association rates using a reliable and accurate model that combines biophysics-based simulations and machine-learning-based bioinformatics algorithm.4 M−1 s−1) is much slower than 3LB6 (1 × 108 M−1 s−1). As a result, neither 4JPK nor 3LB6 has been classified into the correct group by neural network model. The association rate of 4JPK was identified to be above the threshold 4 × 105 M−1 s−1, while the association rate of 3LB6 was identified to be below the threshold. In order to explore the reason why our simulations generated the results that are opposite to the experimental measurements, we plotted the structures of these two complexes in Fig. Although our test proved that the association profiles for most protein complexes can be successfully recognized, there are still possibilities that complexes were classified into wrong groups. Figure 3) were carried out with different random initial configurations based on the reported ionic strength. Their corresponding association profiles were then calculated from the simulations. Using these profiles as inputs, the neural network model was further utilized to estimate whether the association rates of these 10 protein complexes are higher or lower than the optimized threshold (4 × 105 M−1 s−1). During the classification, the association profiles of all the 96 protein complexes in the benchmark and their corresponding experimental association rates were treated as the training set, while each of the 10 protein complexes was fed into the neural network for testing. The prediction was further compared with the real experimental data. As a result, we found that we can correctly predict if the association rates are faster or slower than the threshold for eight protein complexes out of ten, consistent with the cross-validation results.To further test the stability of our classification model and rule out the possibility of over-fitting, an additional test set consisting of 10 protein complexes was independently collected. The detailed information of these complexes can be found in the Table 6 M−1 s−1. Not surprisingly, it has been incorrectly assigned to the class with association rate below the threshold. 1SGN is a complex formed between protein Ovomucoid and Streptogrisin B. A closer structural inspection indicates that the interaction of the complex is formed through an inter-molecular β-sheet  exists as a cooperatively folded helical heterodimer. The association of this type of complexes cannot be simulated by our method. They are thus excluded in the study. Consequently, the number of protein complexes in the benchmark has further been narrowed down from 114 to 102. Coarse-grained Monte-Carlo simulations were carried out for protein complexes of all these remaining entries. However, we only successfully generated simulation trajectories under all different values of distance cutoff for 96 out of 102 protein complexes. The PDB of six entries which failed to complete all the simulation runs are: 1A4Y; 1WQJ; 2B42; 2NY7; 3BT1; and 4K71. Simulations in these systems were aborted under small values of distance cutoff. As a result, a total number of 96 protein complexes were passed into our final prediction model by feeding them into the neural network for association-rate classification. Detailed information about this benchmark set can be found at dc [For each given protein complex in the benchmark, the process of its association from separated binding partners was modeled by a previously developed kinetic Monte-Carlo simulation method. In specific, a coarse-grained model of protein structures is used in the simulation. Comparing with other previous coarse-grained models in which protein sidechains were grouped into either one  or multidc . Specifidc , 76, as dc . Based odc  is appli3 trajectories are carried out. Each trajectory consists of 103 steps and each step is 0.01 ns, so that the total simulation time for each trajectory is 10 ns. Moreover, each trajectory starts from a relatively different initial conformation, including different relative orientation between the interfaces of two binding partners. However, their initial distances are all below the given cutoff value in these trajectories.Practically, this simulation algorithm is performed in parallel under 10 different distance cutoffs from 16 to 25 Å  and false positive rate (FPR) from the summary of each individual complex, as well as the specificity, sensitivity, precision and accuracy of the overall prediction. Detailed evaluation of our cross-validation results is described in the “Results and Discussions”.https://github.com/wulab-github/KonPred. This package contains an executable file predicting if the association rate of two binding partners in an input protein complex is higher than a predefined threshold. It also contains the list of 96 protein complexes in the benchmark set and their calculated association profiles used as input for the neural network model. The package offers an instruction and a demonstration example (PDB 7CEI) of how to obtain the prediction with the templates of both input and output files. The program work on a Linux platform and downloading is free for academic users.The classification program is available for download at: Additional file 1. The Supporting Information contains the model validation for the Monte-Carlo simulations of protein-protein association; the supporting figures from Figure S1 to Figure S6; and supporting Table S1 and Table S2.


However, the experimental methods for systematically mapping of lncRNA–protein interactions remain time-consuming and expensive. Therefore, it is urgent to develop reliable computational methods for predicting lncRNA–protein interactions. In this study, we propose a computational method called LncPNet to predict potential lncRNA–protein interactions by embedding an lncRNA–protein heterogenous network. The experimental results indicate that LncPNet achieves promising performance on benchmark datasets extracted from the NPInter database with an accuracy of 0.930 and area under ROC curve (AUC) of 0.971. In addition, we further compare our method with other eight state-of-the-art methods, and the results illustrate that our method achieves superior prediction performance. LncPNet provides an effective method via a new perspective of representing lncRNA–protein heterogenous network, which will greatly benefit the prediction of lncRNA–protein interactions. The non-coding RNA (ncRNA) plays important roles in biological processes, which can influence human health on various levels . ExistinMost of the functions of lncRNA are still unknown. One of the mechanisms is lncRNAs usually function by binding to chaperone proteins . Hence, In recent years, some methods for predicting lncRNA–protein interactions have been developed. Muppirala et al. applied random forest (RF)  and suppHowever, there are main drawbacks with the aforementioned methods. First, most of their extracted features for proteins as well as lncRNAs are hand-crafted, which consume much time and require strong domain knowledge. What is more, the previous studies attempt to construct a model to predict the lncRNA–protein interactions of all species. All these may lead to low robustness and overly optimistic predictions.With the development of machine learning, network representation learning algorithm has become a pressing research task . In thisIn this study, we apply the known lncRNA–protein interaction data from NPInter v2.0  and lncRAn lncRNA–protein heterogenous network is constructed with lncRNA–lncRNA similarity, protein–protein similarity, and known lncRNA–protein associations. lncRNA–lncRNA similarity and protein–protein similarity are both quantified in two different ways.The Jaccard similarity  is an inBLAST is a fundamental and basic local alignment search tool for sequence similarity based on a local optimal alignment strategy . EssentiThe lncRNA–lncRNA Jaccard similarity network can be represented using a bipartite graph e-value is less than 0.001.The lncRNA–lncRNA BLAST similarity network can be represented using a bipartite graph e-value is less than 0.01.Similarly, two bipartite graphs Then, we construct two heterogenous networks. Among them, one is by known lncRNA–protein interactions, lncRNA–lncRNA similarities, and protein–protein similarities calculated with the Jaccard similarity. The other is by known lncRNA–protein interactions, lncRNA–lncRNA similarities, and protein-protein similarities calculated with BLAST similarity.-path-based random walks to generate paths that can capture both the semantic and structural correlations between different types of nodes and then facilitate the transformation of heterogenous network structures into metapath2vec′s skip-grams.Network embedding can use less information to represent nodes as dense- and low-dimensional vectors and has been rapidly developed and applied recently . AccordiIn detail, a meta-path scheme  follows :p as the SVM kernel function.Precision (PRE), recall (REC), specificity (SPE), accuracy (ACC), Matthew’s correlation coefficient (MCC), and F1-score are the most common classification model evaluation indicators. They can be defined as :PRE=TPTTo evaluate the prediction performance of LncPNet, we test RF , naive BIn order to fully evaluate the performance, we compare LncPNet with three sub-models on NPInter v2.0. LncPNet model construction is mainly divided into three steps. Specifically, we construct a heterogenous network with lncRNA–lncRNA similarities, protein–protein similarities, and known lncRNA–protein interactions, where lncRNA–lncRNA similarities and protein–protein similarities are calculated by the Jaccard similarity and BLAST similarity, respectively. Then, a feature vector is generated from the heterogenous network with network embedding (metapath2vec) to characterize a pair of lncRNA and protein. Finally, with the feature vectors with class labels as inputs, SVM is trained to predict potential lncRNA–protein associations. The construction of heterogenous network contains four types of different strategies. In approach 1, only known lncRNA–protein interactions (KNet) are used to construct the network; in approach 2, known lncRNA–protein interactions and Jaccard similarity (KJNet) are used to construct the network; in approach 3, known lncRNA–protein interactions and BLAST similarity (KBNet) are used to construct the network; and in approach 4, known lncRNA–protein interactions, Jaccard similarity, and BLAST similarity (LncPNet) are used to construct the network. Missing negative samples has always been a problem in predicting molecular interactions, which leads to a wide variety of negative sample generation methods. However, few studies have proved how to generate negative samples is the most reliable. In this section, we summarize three commonly used negative sample construction methods. The first one, and also the most popular one, is the random pairing method. Negative samples are randomly sampled from the possible lncRNA–protein pairs except the positive samples. The second one is the method of subcellular localization, which is based on the assumption that the lncRNA and protein that are not in the same subcellular location would not interact with each other. Therefore, proteins and lncRNAs that are not in one organelle are regarded as negative sample pairs. The third one is the network distance method, which calculates the shortest-path distance between each lncRNA and protein in the prior interaction network, and treats the protein and lncRNA that are greater than a certain distance threshold, for e.g., six, as a negative sample pair.According to these rules, we further categorize the distance method of selecting negative samples into three types of experiments: 1) “Distance_3”: the negative samples with a distance equal to 3; 2) “Distance_5”: the negative samples with a distance greater than 1 and less than or equal to 5; and 3) “Distance_7”: the negative sample with a distance greater than 1 and less than or equal to 7. To avoid the imbalance problem when training the classifier, we choose negative samples with the same number of positive samples in the experiments. As presented in (1) The first type of method is mainly based on sequence information, structural information, evolutionary knowledge, or physical and chemical properties to mine the distinguishing characteristics of the lncRNA and protein. For example, RPI-SE applied the position weight matrix combined with Legendre moments to obtain protein evolutionary information and k-mer sparse matrix to extract feature of lncRNA sequences. SFPEL-LPI used sequence information to build a feature projection ensemble-learning frame to predict lncRNA–protein interactions.(2) The second type of method is mainly to use stacked autoencoders to extract high-level hidden features of proteins and lncRNAs. For example, IPMiner extracted raw sequence composition features from lncRNA and protein sequences, high-level features by applying stacked autoencoder, and fine-tuning features using label information, and then a training ensemble strategy such as RF classifier to robustly predict the interactions between lncRNAs and proteins.(3) The third type of method mainly uses topological information to extract lncRNA and protein features. For example, LPLNP employed a linear neighborhood propagation method, to predict lncRNA–protein interactions. LPBNI used a bipartite network–based method for predicting lncRNA–protein interactions. RWR and CF are also the same type of methods. LPIHN constructed a lncRNA–protein heterogenous network and used a random walk with restart to infer novel lncRNA–protein interactions.In order to further demonstrate the reliability and robustness of prediction by the LncPNet method, we compare LncPNet with the eight state-of-the-art methods, namely IPMiner, RPI-SE, LPLNP, RWR, CF, SFPEL-LPI, LPBNI, and LPIHN, on the same benchmark of NPInter v2.0. These methods are typical methods that have been proposed in recent years, and they can be divided into three categories:We replicate all these methods on the same dataset for fair comparisons. As shown in In order to further evaluate the reliability of our prediction model, we propose a case study to verify its performance. As mentioned earlier, the dataset we used in LncPNet is NPInter v2.0, and currently NPInter has been updated to NPInter v4.0, which includes some novel lncRNA–protein interaction pairs. We test to predict the new lncRNA–protein interactions confirmed in NPInter v4.0 based on known interactions in NPInter v2.0. Specifically, we predict the 23 pairs of interactions newly discovered in NPInter v4.0 and the generated 23 pairs of negative samples and rank them according to the scores. As shown in In this study, we proposed LncPNet based on a heterogeneous network embedding method for predicting lncRNA–protein interactions. The experimental results demonstrated that LncPNet achieves high prediction performance on our benchmark dataset and yields better results compared to other methods. As for the lncRNA–protein interaction predictive task is a nonnegative sample problem, we provided a new perspective into network embedding by comparing three kinds of methods for negative sampling. In addition, the case study results further demonstrated the effectiveness of LncPNet. The network embedding method is a general node representing method. The framework of LncPNet can be expanded to other interaction predictive task, such as miRNA–protein interaction prediction and lncRNA–disease interaction prediction.