Almost all interactions and reactions that occur in living organisms involve proteins. The various biological roles of proteins include, but are not limited to, signal transduction, gene transcription, cell death, immune function, structural support, and catalysis of all the chemical reactions that enable organisms to survive. The varied roles of proteins have led to them being dubbed ‘the workhorses of all living organisms’. This article discusses the functions of proteins and how protein function is studied in a laboratory setting. In this article, we begin by examining the functions of protein domains, followed by a discussion of some of the major classes of proteins based on their function. We consider protein binding in detail, which is central to protein function. We then examine how protein function can be altered through various mechanisms including post-translational modification, and changes to environment, oligomerisation and mutations. Finally, we consider a handful of the techniques employed in the laboratory to understand and measure the function of proteins. Proteins are one of the major classes of macromolecules and are essential for a diverse range of biological functions. The unique amino acid sequence of proteins determines their global 3D structure, which in turn defines their cellular function. Each protein has a distinctive structure and size. To enable a full understanding of the role of proteins within cells, it is necessary to explore protein structure alongside function. We therefore recommend also reading the ‘uncovering protein structure’ review article in this series for an overview of the structural concepts governing protein function, although some structural concepts will also be discussed here in less detail. In this review, we will discuss the functions of protein and their constituent domains, how they bind to their targets, and how post-translational modification, environment, and localisation can affect protein function. We will then consider how mutations and modifications to proteins can have large effects on their function as well as broader biological processes. Importantly, a large portion of this review will focus on the interactions of proteins with other molecules, including other proteins, and how these interactions are studied in the laboratory to further understand protein function.http://geneontology.org/) that associates each protein in a given organism with a molecular function, biological process and cellular compartment. As shown in Before discussing protein function further, it is important to define what exactly is meant here by ‘function’. From an evolutionary perspective, the function of any protein is to enhance the fitness of the host organism. However, in this article we discuss protein function in terms of its specific biological role in the cell. For instance, a protein may provide structural support, move molecules in or out of a cell or cellular compartment, enable communication within an organism or catalyse metabolic reactions. These are all examples of protein function. Given the vast array of protein functions, it should be stated at the outset that the examples discussed in this review are by no means exhaustive. It is instead our hope that the examples given here will provide insight into the large functional repertoire of proteins. A useful resource for appreciating the distribution of proteins by function is the Gene Ontology database . Pyruvate kinase has 3 distinct domains, designated A, B, and C . Domain As illustrated by pyruvate kinase, many domains have an enzymatic function i.e., they catalyse a specific chemical reaction within their active  site. These catalytic domains contain specific amino acids motifs within their active site that influence their catalytic properties including substrate affinity and turnover rate. Typically, catalytic domains are found in multi-domain proteins, as seen for pyruvate kinase. The other domains may be involved in other functions such as regulation of the enzymatic domain or interacting with other biological molecules.As previously highlighted, a large proportion of protein function involves binding to other proteins or biomolecules. In biochemistry, we often refer to the targets of a protein as their binding partners. Given the central role binding plays in protein function, many proteins contain domains that are exclusively involved in binding. Here, we briefly discuss some examples of domains involved in binding. The later protein binding section of this review also contains further information about how association of molecules can further influence their function.Domains that are involved in PPIs are important for bringing multiple proteins together in a highly specific manner. Many of these domains are necessary for the formation of homomeric and heteromeric protein complexes. Examples of these kinds of domains include SH3 domains that bind to proteins that contain proline rich motifs; sterile α motif (SAM) domains that interact with other SAM domains to form homo- or hetero-oligomers; and LIM domains, which comprise contiguous zinc fingers that interact with proteins containing PDZ domains or other LIM domains.Reversible, covalent modifications of residues within domains are essential signals that occur in response to the ever-changing environment within and outside cells. Some examples of modification include phosphorylation, methylation, acetylation, and lipidation . To propagate these signals, it is important for proteins to be able to bind these modified amino acid residues. Domains that bind to modified residues therefore allow rapid and complex PPIs in response to various stimuli, particularly in signal transduction pathways where specific amino acids within a protein are rapidly modified. Domains that bind modified residues are typically components of multi-domain proteins and serve to form PPIs. Some examples of domains that interact with modified residues are listed in An example of a domain that binds a modified residue is the Src Homology 2 (SH2) domain. SH2 domains are phosphotyrosine binding domains that were first identified in the viral protein Src and are thus named Src Homology 2 domains. SH2 domains bind linear peptide motifs containing phosphorylated tyrosine residues and the specificity of the interaction is derived by the residues flanking the target phosphotyrosine. Structurally, SH2 domains tend to be highly conserved, comprising three central β-strands flanked by two α-helices . ApproxiIn addition to mediating PPIs, which we abbreviate to PPIs, the function of a domain may depend on interactions with other macromolecules, such as nucleic acids or lipids. Interactions between proteins and these other molecules are important for a myriad of biological processes including catalysis, gene transcription and translation. Additionally, such interactions are key for sequestering or localising proteins to certain cellular compartments. For example, some protein–lipid interactions embed proteins in specific membranes within the cell. Some examples of domains that form interactions with other molecules are shown in Multi-domain proteins, particularly those with enzymatic function often contain regulatory domains. The role of these domains is to specifically regulate catalysis. An example of a regulatory domain was exemplified in Full-length proteins can be made up of one or multiple domains that give rise to the overall function of a protein . In thisCells rely on enzymes to catalyse all reactions that are essential for life. Enzymes typically bind molecules  and facilitate a chemical reaction. We suggest referring to the enzymes review in this series to learn more about the main categories of enzymes and the reactions they catalyse. Enzymes work by lowering the activation energy of a reaction, allowing the reaction rate to be increased. In many cases, an enzyme requires cofactors or coenzymes to function. Coenzymes are organic molecules mostly derived from vitamins whereas cofactors are inorganic species, such as the metal ion magnesium. Humans and some other animals cannot synthesise all coenzymes necessary for life and they must be ingested through a balanced diet. The term ‘apoenzyme’ is used to denote an enzyme that is not bound to a cofactor or coenzyme, and likewise, ‘holoenzyme’ is used to refer to the bound state.As an example, the enzyme methylmalonyl-CoA mutase requires vitamin B12 for its isomerisation reaction, and branched-chain 2-oxo acid dehydrogenase is inactive without its cofactor vitamin B1.Some of the most abundant proteins in nature have structural functions within cells and organisms. These proteins are essential for both the formation and maintenance of cellular structure, forming extensive networks particularly in eukaryotic cells. In humans, extracellular collagen is the principal protein of connective tissues and accounts for approximately 25% of total protein mass, highlighting the abundance and importance of these structural proteins. In addition to maintaining cellular structure, a number of structural proteins are also essential for the distribution of organelles throughout the cytoplasm and play an important role in alignment of the chromosomes along the cell centromere prior to cytokinesis during cell division. Storage proteins are important for sequestering metal ions and as a source of amino acids. Ferritin is an example protein which is produced by almost all living organisms and is important for storing iron . FerritiSignalling proteins are essential for cell function as they allow cells to communicate with one another and respond to cues from their environment. Signalling proteins can be found in all compartments of the cell, including the plasma membrane, the cytoplasm, the nucleus, and the mitochondria. Receptors detect signals and are often found in the plasma membrane, as many signals cannot cross the membrane. However, some receptors, like those for the estrogen hormone are localised in the cytosol, as these hormones can cross the plasma membrane. Binding of a ligand to the extracellular portion of membrane receptors leads to a change inside the cell that relays the signal from outside. This process is termed signal transduction and allows cells to communicate with one another and respond to cues from their environment in a controlled manner. Signalling proteins often contain domains that bind modified residues as discussed in the first section of this review. An example of this is the signal transducer and activator of transcription (STAT) family of proteins. The STAT proteins contain SH2 domains that bind to phosphorylated residues on receptors inside the cell, and upon binding, they too become phosphorylated at tyrosine residues, which leads to a conformational change that ultimately allows them to translocate into the nucleus, bind DNA and up-regulate DNA transcription.2 binding to haemoglobin. Some examples of transport proteins are detailed in All cells must transport molecules between various cellular compartments, across the plasma membrane, and in the case of multicellular organisms, between cells. Ions such as sodium and potassium, proteins such as neurotransmitters and sugars such as glucose all need to be transported in some way. Channels, pumps, and pores transport specific molecules across membranes. Molecules can also be transported within cells or throughout the body by binding to transport proteins, for example OAlmost all proteins require regulation to ensure they perform their function at the right time and place. We have already seen how domains can mediate PPIs and how protein modifications can allow switching between functional states. Regulation of gene expression is an excellent example how regulatory proteins control a central cellular function. Here, transcription factors and other DNA-binding proteins interact with DNA to control when and to what extent a gene is transcribed and/or which epigenetic signals are made on the DNA. Regulatory proteins can also regulate the flux of a signalling or metabolic pathway, often significantly altering biological outcomes. Other regulatory proteins include molecules such as cyclins, which control progress through the cell cycle. Cyclins activate cyclin dependent kinases which phosphorylate the proteins that drive the cell cycle, such as the DNA replication machinery. Many proteins’ primary function is to regulate other pathways or functions in the cell, thus maintaining homeostasis. Below we use the example of phosphofrutokinase-2/fructose bisphosphatase-2 to illustrate the role of a regulatory protein in maintaining homeostasis in a biological system .Streptococcus pyogenes contain an elaborate molecular defence system known as the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system. CRISPR relies on the RNA-guided endonuclease Cas9 to modify the DNA of invading viruses and plasmids and has become a commonly used gene editing tool in modern molecular biology. Antibodies, or immunoglobulins, are defence proteins produced by B-cells that circulate in the blood. They form an essential component of the adaptive immune system by binding to foreign molecules and are the basis for vaccination. Antibodies are also a great example of how the overall function of a protein differs from the function of its constituent parts. Antibodies contain regions that specifically recognise antigens (foreign substance) through binding, and regions that regulate the response to antigen binding. So, while the different domains that comprise the antibody function by binding or regulation, the overall function of the antibody is defence as part of the immune system.Defence proteins are found in a wide variety of organisms, and despite mechanistic diversity, all act to defend an organism from foreign attack. The venoms of snakes and spiders contain a cocktail of defence proteins that disable predators, enabling escape. Bacteria such as This section presented some examples of the range of functional domains in proteins and described the categorisation of proteins based on their function. We hope this section has provided the foundation for understanding the following sections, which will focus on the role of binding in protein function, factors that influence protein function and how we measure protein function in the laboratory.As we have seen in prior sections, most proteins can bind reversibly through non-covalent interactions to their binding partners. These binding partners may be other proteins, small ligands, nucleic acids, lipids, or sugars that occur inside or outside of the cell. It is these interactions that bring about the molecular processes essential for life. They allow chemical reactions to occur, large complex machineries to form and signalling within and between cells. Binding is also crucial for the regulation of almost all of these processes which are in constant flux during life. Below we examine protein binding in more detail, starting with the functional consequences of binding.The physical binding of a protein to its binding partner can have direct functional consequences by modifying the structure or accessibility to binding or catalytic sites. Conformational changes that occur in a protein at a distant site due to binding of another ‘regulatory’ molecule are an example of allostery . Here, the allosteric binding event may cause a change in the configuration of the protein’s active site and thus ability to catalyse a reaction (as seen for pyruvate kinase) or a change in one of its binding surfaces and ability to bind another binding partner. Allosteric binding is a common way to regulate protein function. A different functional consequence of binding can be to physically change the subcellular location of the protein by virtue of the location of its binding partner, i.e. binding to lipids can bring a cytosolic protein to the cellular membrane. Finally, a post-translational modification of a protein also facilitates a change in subcellular location. This is common for transcription factors which may be modified in the cytoplasm, which allows for an interaction with a factor that facilitates nuclear import, where they then carry out their function as discussed above for the STAT proteins.2 and involve approximately 70% direct and 30% water mediated contacts. For PPIs, the interacting proteins have good structural and chemical complementarity that involve hydrophobic, polar, or charged interactions. Usually, long-range electrostatic interactions are most important for acting at a distance to enable the initial binding events during association and hydrogen-bonds are most important for the final short-range binding events and need to be first broken during dissociation. A PPI can occur when a protein domain binds to another domain over a broad interface or when it binds to a short disordered linear peptide motif, which may bury less surface area . Disordered peptide interactions are frequently used as they can easily and quickly form the required functional interaction since they have fewer 3D structural requirements compared to a folded domain. As we have seen, there are many protein binding domains that specialise in recognising short, disordered peptides in this manner and usually involve large changes in peptide conformation. The exact nature of the interacting protein surfaces and associated required conformational changes will determine the binding thermodynamics and kinetics which is considered next.In general, protein interactions occur using binding surfaces that are between 1200 and 5000 ÅKd with molar units). The smaller the Kd value (i.e. picomolar versus millimolar), the tighter the binding. The dissociation constant is directly related the thermodynamic quantity, the standard free energy of binding (ΔGo). When an additional hydrogen bond is added to an interaction, this results in a small change in ΔGo but a larger change in Kd. Protein interaction strength spans a wide spectrum from weak to strong and this range of binding affinities mediate a range of functions. To understand the free energy of binding in more detail, similar to any change in free energy, ΔGo can be calculated in terms of the change in entropy and change in enthalpy. The change in binding enthalpy reflects the making and breaking of bonds during binding. Many binding events are exothermic as many new bonds are formed, although enthalpy changes are hard to predict, especially when conformation changes are required for binding. The change in binding entropy reflects the change in disorder of the molecules involved with many binding events being entropically favourable even though two free components combine into one component which is more ‘ordered’. However, it is the entropy of the entire system to consider that usually includes dynamic water molecules being released at the interface of a complex. As such, it is the balance between entropy and enthalpy changes that determines the overall affinity. In general, more extensive binding surfaces have greater opportunities for amino acid interactions between the two proteins and will therefore result in stronger binding. However, the binding thermodynamics is not the only consideration as interactions rarely reach equilibrium in the cell and we need to also consider binding kinetics. The kinetics tells us how fast the protein and target bind (associate) and how slow the protein–target complex fall apart (dissociates). Related to this is the time it takes for half of the protein–target complex to dissociate, called the half-life of the complex and is calculated as 0.693 divided by the dissociation rate constant. Thermodynamics and kinetics are linked as reflected in the formula where Kd is the ratio of the dissociation and the association rate constants, which we explore again later when we consider the design of drugs to modify protein function. With these key parameters in mind, we next define the two major PPI categories; transient and obligatory PPIs.Like protein folding, protein binding obeys the laws of thermodynamics and kinetics, enabling comparison and categorisation of different protein interactions. For a description of thermodynamics basics, it would be useful to read the protein folding section in the uncovering protein structure review of this series. The strength of the interaction between protein and its target is described by the size of the dissociation constant  with much longer half-lives of up to days and dK values in the sub picomolar range. Regardless of their affinity, the examples discussed are considered transient PPIs as the interacting proteins are not always bound together and often exist on their own. Transient multi-protein complexes do also exist and are frequently found as part of cell signalling pathways, where protein interactions rapidly change in response to external signals and reversible post-translational modifications. These are generally referred to as dynamic molecular assemblies and are most challenging to study since they continuously change protein composition and can be very large and difficult to isolate from cells.Transient interactions are reversable, usually short-lived and may be weak or strong. For example, some weak binding transient interactions include reversible cell-to-cell contacts or transient signalling complexes and only exist together with half-lives of milliseconds and Escherichia coli proteins are oligomers  and the majority are symmetric homooligomers, where the same protein chain  self-associates to form larger structures. For example, two subunits associate together to form a dimer, four subunits associate to form a tetramer, or twelve subunits associate together to form a dodecamer etc. Oligomeric proteins stay folded and are more stable than monomeric proteins, which protects them from degradation and aggregation, that usually require the unfolded chain to be exposed. Forming oligomeric proteins in a step-by-step process allows them to bind their targets cooperatively, which is a mechanism for increasing binding specificity (see section below). Furthermore, the subunit interfaces between two existing subunits evolve faster than the evolution of a new protein fold as subunit interfaces can easily form flexible active sites for enzymes or allosteric sites to regulate the overall structure and activity of the oligomeric protein. Non-symmetric, obligatory protein complexes also exist such as ribosomes, proteosomes, nuclear pores, chaperones, nucleosomes, and multi-enzyme complexes such as pyruvate dehydrogenase all of which contain a defined number of different proteins in a stable complex. These can make complex machines that have a defined function in the cell. Examples of both obligatory and transient protein interactions can be found in While reversible transient interactions are abundant, arguably, the most important protein interactions involve obligatory interactions. Proteins involved in these interactions do not exist on their own and instead self-associate to permanently exist as multiprotein assemblies or oligomers that define a proteins quaternary structure. Over 75% of A summary of the different types of protein interactions, surfaces, thermodynamics, and kinetics is illustrated in Inside the cell, many binding partners have high concentrations, which helps them find their targets quickly since the association rates is dependent on protein concentration. However, one factor that puts an upper limit to the association rate is the physical rate of diffusion that dictates the speed at which protein and target molecules move and collide with each other inside the aqueous environment of the cell. Another factor is whether there are available mechanisms for the protein and target to approach each other in the correct orientation so that the target sticks and does not simply bounce off and so has to try and collide with the protein again. This can be somewhat tricky for enzymes that have small active sites that are within a narrow crevice. Therefore, to increase the chance of a collision, the binding site may need a wider opening to be more exposed to allow targets to bind from multiple directions and orientations. Interestingly for some enzymes such as glucokinase, the enzyme partially populates an open conformation with a more exposed active site allowing functional catalytic rates of glucose conversion to glucose-6-phosphate.Proteins can also use long-range electrostatic interactions and a binding intermediate called the encounter complex to search the cell more efficiently for its specific partner. When a protein with net charge approaches close to its target that has an opposite charge, long-range attractive electrostatic interactions help it steer to the binding surface . ElectroTaken together, long-range electrostatic interactions and encounter complexes are both useful mechanisms to allow some proteins to find their partners faster. In many cases it will allow proteins to reduce their search from searching in three dimensions to two dimensions (i.e. searching within a charged membrane plane) or even one dimension . In these cases, an initial  contact can be made rapidly with the search location  to form an encounter complex. Then, the protein can diffuse within the search location, which overall will be a smaller region to search as opposed to the whole 3D cell .We have explored both thermodynamics and kinetics of protein interactions and how both favourable binding affinity and kinetics can be attained. However, the concept called macromolecular crowding brought about by the very high concentration of molecules inside the cell should also be considered. Macromolecular crowding means a protein will physically be in a smaller volume since the other molecules take up so much space and this alters its properties. For example, it will increase how much an individual protein associates with other proteins (target and non-target) but actually reduces the rate of association with its specific target (particularly for larger proteins). An analogy would be if you were trying to find a friend at a crowded party. If you simply randomly walk around, before you find your friend, you will bump into many other people. As such we must take into account how well proteins distinguish between targets and non-targets, i.e. the specificity of these interactions to understand the situation inside the cell.Kd) and the related ΔGo or free energy of binding, however given that non-specific targets may also bind to a given protein, we must also define the free energy of ‘specificity’ for the interaction. The ‘specificity’ of the interaction is a relative thermodynamic term that tells us how well a given protein binds to its target compared with other potential targets in the cell  are reversible or irreversible modifications made to proteins that occurs after their translation . PTMs arProtein phosphorylation is the addition of a negatively charged phosphate group to specific residues on proteins. It has been estimated that approximately 30% of eukaryotic proteins can be phosphorylated on at least one residue, making protein phosphorylation a common form of PTM. Phosphorylation predominately occurs on serine, threonine or tyrosine residues, with approximately 85% of phosphorylation in eukaryotes occurring on serine residues, and 12% and 2% on threonine and tyrosine residues, respectively. Phosphorylation events often serve to activate or inactivate protein activity, particularly in signal transduction where it is the most common form of PTM. The addition of the phosphate groups onto these residues changes the protein's surface charge, which in turn alters interactions with other proteins as well as interactions between residues within the protein itself. Kinases are enzymes that catalyse phosphorylation and phosphatases catalyse dephosphorylation. It is the balance between kinase and phosphatase activity that controls the constantly fluctuating phosphorylation level in the cell. Phosphorylation events are critical in cell cycle control, growth, signal transduction, apoptosis, and many more biological events.Ubiquitination involves the addition of a small protein called ubiquitin on to other proteins. This process involves a large family of proteins, the E2 and E3 ligases, that add ubiquitin molecules on to proteins, adaptor proteins that regulate ubiquitination, and deubiquitinating enzymes (DUBs) that reverse this process, removing ubiquitin chains. Ubiquitination of proteins is involved in the regulation of their stability, activity, localisation, and PPIs. Proteins can be mono- or poly-ubiquitinated, and different types of ubiquitin chains determine the effect ubiquitination has on the protein. Polyubiquitin chains consist of ubiquitin monomers that join via peptide or isopeptide bonds to form chains. Ubiquitination is most commonly associated with degradation, where the ubiquitin targets the protein either to the proteasome for degradation. However, there are also a number of other functional outcomes associated with ubiquitination. Protein acetylation is one of the most abundant forms of PTM. Acetylation involves the transfer of acetyl groups from acetyl-CoA on to specific sites on polypeptide chains. Acetylation can occur on thiol groups (sulfur), hydroxyl groups (oxygen) and amino groups (nitrogen). Histone acetyl transferases and histone deacetylase enzymes play an essential role in the regulation of chromatin structure. Histone acetyl transferases are generally considered transcriptional coactivators as acetylation of lysine residues on histones is associated with relaxation of chromatin structure. Conversely, histone deacetylases, which remove acetyl groups from lysine residues, cause chromatin to compact and so are thought of as repressors. The balance of the actions of these proteins is essential in the regulation of gene expression. Histone acetyl transferases also target non-histone proteins including cytoskeletal proteins in the cytoplasm and transcription factors. Acetylation is also important for PPIs, metabolism, and protein localisation.Lipidation involves the addition of lipid moieties to proteins . This maIn addition to coordinated and controlled modification of protein function as discussed in the sections above, protein function can also change due to mutations, or changes in the sequence that codes for the protein. Unlike other forms of modification, mutations are uncontrolled and, in some cases, are detrimental to protein function. Mutations vary in their impact and may substantially alter protein function or have minimal or no detectable effect. The nature of the mutation and its position in the protein’s sequence both influence its effect on overall function.For example, a mutation that occurs in the active site of an enzyme is more likely to have a severe effect on function than a mutation in a non-conserved region of the protein.Mutations can occur spontaneously or be generated through errors in replication, during cell division or during DNA repair, or due to mutagenic damage to DNA. Mutations can be classified structurally, by their impact on the protein’s sequence, or by the functional impact on proteins. Structurally, mutations can occur at small-scale, affecting a small number of nucleotides through insertion, deletion, or substitutions of bases. Alternatively, mutations can involve large-scale modifications, through amplification, deletion, rearrangement, or fusion of long sections of DNA or chromosomes. BCR-ABL is an example of a fusion gene caused by reciprocal translocation of chromosomes 9 and 22. This fusion forms a constitutively active protein that induces uncontrolled cell division and the development of chronic myeloid leukemia.Missense mutations produce substitution of one amino acid for another. This may change the protein’s function, depending on the location of the mutation and whether the change is conservative Nonsense mutations occur when a change in amino acid results in the incorporation of a premature stop codon. This results in incomplete translation of the protein and will generally significantly disrupt its function. However, nonsense mutations near the C-terminal end of the protein sequence may enable normal function to be maintained.Silent mutations have no effect on the protein function as they involve no change in amino acid sequence, thus the overall structure of the protein remains the same despite the gene sequence itself being mutated.Frameshift mutations occur when there is a deletion or insertion of a number of bases not divisible by three. As codons are formed from triplets of bases, insertions and deletions alter the entire reading frame downstream, resulting in altered codons and so amino acid sequence. The size and location within the sequence determines the extent that the frameshift will affect protein function.Mutations within the coding regions of genes can change the resulting protein sequence. Such changes are classified into the four following categories: The alterations caused by missense, nonsense, silent, and frameshift mutations are depicted in The types of mutations discussed in the above sections are also classified according to their functional effect on proteins. Some mutations hamper the protein's function and are referred to as ‘loss of function’ mutations; likewise, mutations that enhance function are called ‘gain of function’. Using the Janus Kinase (JAK) proteins as an example we can examine the difference between a loss or gain of function mutation. The JAKs are a type of enzyme called a kinase and catalyse the addition of phosphate groups onto tyrosine residues in other signalling proteins. The C759R mutation in the JAK3 kinase  domain results in inactivation of its kinase activity, leading to severe combined immunodeficiency (SCID) due to decreased immune signalling downstream of JAK3. Conversely, the V617F mutation in the regulatory domain of JAK2, generates an increase in kinase activity, resulting in over-activity and an association with blood cancers.Some mutant genes can disrupt or override the function of wildtype proteins and so are ‘dominant-negative’. Nonsense mutations that induce the loss of a functional domain but retain a substrate binding domain exemplify this concept – the mutant will retain the ability to bind to its substrate but will block wild-type activity. This can occur, for example, if a transcription factor loses an activation domain, but retains its DNA binding domain. The mutant transcription factor will block the wildtype form from binding to its site (and thus is dominant) and will result in overall reduced transcription due to the absence of its activation domain .These are simplified examples of how mutations in proteins can activate or inactivate protein function. Mutations are essential for driving the evolution of new protein functions. While the above section focused on mutations that can influence protein function during an organism's lifetime, mutations can also alter protein function over many generations through the process of evolution. Evolution allows the introduction of new protein functions into populations as well as adaptation of function to new niches or stressors. Many mutations have a harmful or deleterious effect on protein function, resulting in reduced host fitness. However, some mutations are beneficial, resulting in improved protein function or the development of a novel, useful function that enhances the fitness of the host organisms. For example, a mutation that produces a protein that engenders antibiotic resistance in a bacterium would improve the fitness of that bacterium, allowing it to survive, and pass on this novel protein function to its descendants. Some mutations are neutral, neither improving nor harming host fitness, but instead generating random variability that is known as ‘genetic drift’. Because there is strong selective pressure for active sites and functional motifs within proteins to be evolutionary conserved, diversity in protein function is often driven by gene duplication events. Duplication of a gene allows the function of the original gene to be maintained whilst the duplicate gene can be mutated, potentially generating novel protein forms, without harming the organism.As a protein accumulates mutations over time, it becomes more different in sequence, structure, and function from its ancestor. Comparison of sequence and structure can therefore be used to define evolutionary and functional relationships between proteins. Proteins that share a common ancestor are often termed homologues, orthologues, or paralogues. While most proteins have evolved towards one specific function, some proteins have also evolved additional functions. These proteins have been dubbed moonlighting proteins or are said to have moonlighting functions. This term comes from the term moonlighting used to describe having a second job in addition to your regular employment. See Another example to illustrate the functional consequences of protein evolution is to consider extremophiles – organisms that can survive inhospitable environments such as extreme heat, cold, basicity, salinity, pressure, or radiation. Surviving in such conditions requires that proteins adapt to function in conditions that would otherwise denature them or otherwise severely limit activity. For example, high temperature typically causes irreversible unfolding of protein structure via exposure of the hydrophobic core of the protein. The heat-resistant proteins of thermophiles have evolved increases rigidity and stability, through forming higher-order oligomeric states or the introduction of stabilising salt bridges or disulfide bonds. Such mutations promote tighter protein packing and resistance to unfolding. This exemplifies how the accumulation of the simple mutations discussed in this section can lead to consequential changes to protein function, and so engender changes at an evolutionary scale.Kd) as protein binding to target molecules is a dynamic process, with frequent association and dissociation events occurring. Thus the values for the drug association and dissociation rates are an important factor in drug development. As discussed earlier, since Kd is a ratio of these two values, a drug or any other target may bind a protein with the same affinity but can do so having fast association with fast dissociation or slow association with slow dissociation. In cell signalling, it is frequently desirable to have rapid association and dissociation at low ligand concentrations. However, for a drug, sometimes it is important for the drug to stay bound for a longer time so that frequent dosing is not required  can be inhibited using a drug that changes the conformation of its active site or blocks binding to its substrate. One way of designing such a drug is through a process called structure-based drug design. This approach depends on analysing high-resolution structures of a protein to identify regions that can bind a small molecule inhibitor to have the desired effect. This has been a successful approach for developing drugs for HIV using HIV protease as a target. Drug companies are often interested in calculating the free energy of specificity which helps predict the likelihood of a drug having an unwanted side-effect by binding to other unintended proteins. One must also consider the kinetic profile of the protein:drug interaction . UniProt is a comprehensive resource of key details related to proteins, including sequence, localisation, and function amongst many others. Other important computational approaches rely on comparing amino acid sequences between proteins. This allows the identification of closely related proteins or regions of conserved sequence, providing essential clues relating to function. Such analysis can also employ comparisons of protein folds or structures.A significant breakthrough in computational biology was achieved in 2021 with the advent of AlphaFold, a new and powerful artificial intelligence tool. AlphaFold compares known protein sequences and structures to enable highly accurate predictions of most 3D protein structures from the amino acid sequence. Additionally, more recently it has enabled structural predictions of PPIs.Structures provide unparalleled insights into how proteins work and accordingly there are several computation tools that enable deep analysis of structures. Perhaps most importantly, molecular graphics programmes such as Pymol allow complex structural data to be visualised and manipulated, allowing this information to be interpreted. Other programmes can identify cavities, channels, patches, or pockets that may form binding or catalytic sites, or depict charges and hydrophobicity distributions that may control binding kinetics. Structural bioinformatic analysis is powerful, however, not all proteins have a stable and defined 3D structure. Such proteins, known as ‘intrinsically disordered proteins’ are more difficult to interrogate with the tools described here. Bioinformatic tools have become impactful and indispensable in modern protein science, especially in the early stages of investigation. However, experimental approaches remain essential to validate computational predictions and to enable a full understanding of protein function.Given that most proteins interact with other molecules in one way or another, it can be useful to investigate the nature, affinity, and functional consequences of protein interactions. Each technique has advantages and disadvantages and informs on different aspects of the interactions between protein and other molecules. Here, we will discuss the theory behind some techniques and how they allow us to probe protein interactions. While this does not represent the full repertoire of techniques that allow these kinds of interactions to be studied, we hope discussion of these techniques will provide some useful examples of some core approaches employed in the laboratory.Phage display is a molecular biology technique for identifying molecules that interact with a protein. Phage display involves insertion of a gene encoding a peptide or protein of interest within the bacteriophage coat protein, causing the phage to display the protein on its surface. Large ‘phage display libraries’ can be made to rapidly screen many proteins against a target simultaneously. Binding between proteins in the library and a target is determined by a process termed biopanning. This involves immobilisation of the potential binding target, and subsequent incubation of this target with the phage display library. This is followed by cycles of washing, amplification, and reselection. The specific protein binding partner can be revealed by isolation and sequencing of the population of target-bound phage. This process results in the isolation of peptides, or proteins that are specific for a molecule of interest. This technique is depicted in Fluorescence is a natural property that enables certain molecules to absorb light at specific wavelengths before subsequent emission of light at a longer wavelength. Fluorescent techniques are extensively used in molecular and cell biology, particularly in concert with spectroscopy and microscopy. Fluorescence indicators and labels allow molecules to be visualised or identified in cells and for a variety of kinetic and thermodynamic spectroscopic experiments to be carried out. Furthermore, more complex fluorescent properties, such as energy transfer, polarisation and fluorescence lifetimes are exploited in more advanced techniques. Here we will briefly discuss fluorescent proteins and a powerful fluorescent technique for investigating protein interactions, known as FRET.Aequorea victoria in the 1960s. Molecular biology methods allow GFP to be fused to proteins of interest, enabling fluorescence to be used as a gene expression reporter, biosensor or indicator of localisation using microscopy. GFP has been mutated to produce a variety of other fluorescent colours, such as red fluorescence protein (RFP) and yellow fluorescence protein (YFP) amongst many others. Fluorescent localisation experiments illustrate the power of GFP-fusions. The localisation of a protein within a cell can influence and inform upon its function, and this makes it useful for determining spatiotemporal information about a protein. For example, proteins in the nucleus typically have different functions to those found at the cell surface, and indeed proteins that translocate from one cellular compartment to another may exhibit different functions in different parts of the cell. By genetically engineering a GFP fusion to the N- or C- terminus of a protein of interest, it is straight forward to observe its position within a cell. Cells typically survive illumination of fluorescence proteins, facilitating live-cell imaging over periods of time. Several fluorescence fusions proteins of different colours can be used simultaneously to allow co-localisation experiments. Fluorescent fusion protein experiments require careful controls, as a potential caveat to their use is the modifications they can induce in their fusion partners. For example, many fluorescent proteins oligomerise and may induce unintended oligomerisation or aggregation of the target protein. Fluorescent proteins are also large, and this may affect the normal function or localisation of the fusion partner.Green fluorescent protein (GFP) is a naturally fluorescent protein, that was first isolated from the jellyfish Fluorescent resonance energy transfer (FRET) is a fluorescence-based technique to measure interactions between two molecules. FRET involves two fluorophores that act as a pair. One fluorophore is known as the ‘donor’. When in an excited state, the donor transfers its excitation energy to its pair fluorophore, the ‘acceptor’. This energy is then measured as emitted light from the acceptor fluorophore . This trkon can be determined. This part of the experiment is termed the association phase, and during this phase, a plateau will be reached when the association and dissociation of molecules are in equilibrium. Following the association phase, buffer is flowed over the chip again and the molecules will begin to dissociate, allowing koff to be determined. The final step involves regeneration which will remove any bound molecules so the process can be repeated. The data generated by SPR is called a sensorgram, which provides information about association (kon) and dissociation (koff), which can be used to determine affinity of an interaction (Kd). Sensorgrams will be measured at multiple analyte concentrations, and from the collective data, interaction kinetics can be accurately determined for kon, koff, and Kd. While this technique provides a lot of useful information, it is limited in that you must be able to recombinantly produce your protein, and it must be stable and pure in order to accurately measure binding.Surface plasmon resonance (SPR) provides information about affinity and binding kinetics between molecules. The basic idea of SPR is that binding of two molecules will change the refractive index which can be measured. In a simple SPR experiment, a protein, peptide, or small molecule is immobilised on a chip. Buffer is then flowed over the chip to determine the baseline refractive index. Following this, a second molecule  is flowed over the chip and if the two molecules interact, there will be a change in the refractive index, and from this the Kd), stoichiometry and binding enthalpy to be directly determined and the binding entropy determined indirectly. As with SPR, ITC also required recombinantly produced protein that is stable and pure.Isothermal titration calorimetry (ITC) is a technique that allows the thermodynamics of interactions between molecules in solution to be measured. In the context of proteins, ITC is useful for determining the binding constant, reaction stiochiometry, enthalpy and entropy of interactions of a protein with a ligand such as a peptide, another protein, or small molecule. ITC works by measuring heat released or absorbed when two molecules interact with one another using a microcalorimeter. This microcalorimeter contains two cells, one of which contains water and acts as a reference cell, and the other cell which contains the protein (the sample cell). A syringe is used to inject a series of small volumes of the ligand into the sample cell. If there is an interaction between the ligand and protein of interest the heat in the sample cell will change, and the machine will supply power to return this sample cell to the same temperature as the reference cell. As more ligand is injected into the sample cell, the protein will become saturated and so there will be less of a heat change. The resulting output from this is a series of peaks. The area under each peak is integrated and plotted against the molar ratio of ligand to protein. These data are then fitted to a binding curve which allows the affinity , which is released from various cells in the body and binds to the cell surface receptor, IL6ra. Upon binding to IL6Ra, the IL-6:IL6ra complex can then associate with another receptor, glycoprotein 130 (GP130). GP130 is a transmembrane protein, with an extracellular portion and an intracellular portion (IL6ra does not contain an intracellular portion). The extracellular portion forms the ternary complex with IL-6 and IL6Ra, while the intracellular portion is essential from downstream signalling inside the cell. Formation of the IL-6:IL6Ra:GP130 signalling complex at the cell surface leads to activation of the JAK proteins inside the cell as they come into close proximity. The JAK proteins are constitutively associated with the GP130 receptor tail and they activate one another by transphoshorylation of specific tyrosine residues within their kinase domain. Once activated, the JAK proteins can then phosphorylate other nearby proteins, including the GP130 receptor tail which contains several tyrosine residues that can be phosphorylated. These phosphotyrosine residues can then act as docking sites for proteins that contain SH2 domains such as the signal transducer and activator of transcription (STAT) proteins. Binding of the STATs to the receptor via their SH2 domain results in phosphorylation of the STAT at a C-terminal tyrosine residue. This phosphorylation event leads to a conformational change into their active form and allows the phosphorylated STATs to dimerise. Once in their activated dimer form, they translocate into the nucleus where they bind to regulatory regions on target DNA and up-regulate gene transcription. These changes in gene transcription lead to the eventual biological outcome whether that be differentiation, division, inflammation etc. One of the genes that is up-regulated encodes the suppressor of cytokine signalling (SOCS) proteins which are negative regulators of the JAK-STAT pathway. The SOCS proteins are multi domain proteins – they comprise a variable N-terminal region, a central SH2 domain and a c-terminal SOCS box. The SOCS proteins can directly inhibit the JAK protein activity by inserting an inhibitory region into the active site of the JAK via a region in the N-terminal (for SOCS1 and SOCS3). In the case of SOCS3, specificity for regulating specific cytokines comes from binding of the SH2 domain to the GP130 cytokine receptor. Alternatively, the SOCS proteins that do not contain this inhibitory region can recruit an E3 ligase complex via their SOCS box to the receptor/JAK. This E3 ligase domain leads to ubiquitination of the JAK-STAT proteins, tagging them for degradation. Other proteins that regulate the JAK-STAT pathway include phosphatases which remove the phosphate groups from activated proteins. It is important for these proteins to regulate signalling, as too much or too little signalling can lead to disease. Mutations in the proteins of the JAK-STAT pathway are therefore linked with several haematological, immunological and inflammatory diseases. As such, there have been drugs and therapies designed to target proteins involved in cytokine signalling to treat various diseases. For example, the JAK2 V617F mutation is the most common mutation in a group of diseases called the myeloproliferative neoplasms. These diseases are driven by increased and uncontrolled signalling downstream of JAK2, and so kinase inhibitors such as ruxolitinib bind to the active site of the JAK2 kinase domain and inhibit their activity.Functions of protein domains and the range of protein functionsHow evolution has shaped protein functionThe range of protein interactions and how they shape functionHow post-translational modifications can influence protein functionHow environment, oligomerisation, splicing, and mutations can lead to changes in protein functionSome of the methods we use to measure protein function in the laboratoryWe hope this review has helped with understanding of the following concepts:


We present here a freely available web-based database, called BioMThermDB 1.0, of thermophysical and dynamic properties of various proteins and their aqueous solutions. It contains the hydrodynamic radius, electrophoretic mobility, zeta potential, self-diffusion coefficient, solution viscosity, and cloud-point temperature, as well as the conditions for those determinations and details of the experimental method. It can facilitate the meta-analysis and visualization of data, can enable comparisons, and may be useful for comparing theoretical model predictions with experiments. They govern almost all biological processes that define living organisms the way they are. Knowledge of the physical properties of protein solutions can have practical importance for formulating biological agents and drugs ,2. In orBioMThermDB 1.0 provides thermophysical and thermodynamic data predominantly for globular proteins  and antibodies but information about other proteins is also available see . Each enBioMThermDB 1.0 currently is comprised of 5889 specific entries, of which 77.4% belong to globular and other proteins, and the remaining 22.6% are represented by anti-bodies. More than half of all listed protein solutions have their viscosity measured , which m−1, as they together represent 75.15% of all the data. This is due to the well-known fact that interparticle interactions are usually studied in dilute systems; on the other hand, experiments for observing, e.g., liquid–liquid phase separation, are mostly performed at concentrations above 90 mg mL−1 [ mg mL−1 ,13,14.Similar to concentration regimes, BioMThermDB 1.0 covers protein thermophysical data throughout the whole pH range, with experiments carried out even in the harshest known conditions (pH below 2 and above 10), as displayed in Regarding temperature, one can find most entries (43.57%) at room temperature C consideAnother important physical property that can be used to shorten the time needed to produce trial-protein formulations and help one to both optimize and assess their long-term stability is the zeta potential. The distribution of this indicator of the protein surface charge, as depicted in https://phys-biol-modeling.fkkt.uni-lj.si/biomthermdb.html (current version of 5 October 2022). The editing and ordering of BioMThermDB 1.0 entries, their statistical analysis , and the final visualization were performed in Microsoft Excel. At the moment, BioMThermDB 1.0 is in tabular form but the database will continue to grow and be upgraded. The development of an efficient data browser is also planned for it in the near future; it will be maintained on a regular basis and both all novelties and upgrades will be published on the BioMThermDB 1.0 homepage.BioMThermDB 1.0 is currently developed using the HTML programming language and is freely accessible at To understand and model the stability of protein solutions, a wide and diverse set of information on the thermophysical and thermodynamic properties of proteins in aqueous solutions is of critical importance. Despite the fact that the thermophysical properties of protein solutions are widely studied, the data are scattered in literature and often not consistent due to different protein batches and different experimental techniques. The BioMThermDB 1.0 database presents an overview of the existing published data and some of our own unpublished thermodynamic and thermophysical data on protein solutions that should help scientists in the theoretical treatment of these systems, as well as help experimentalists in planning new experiments.


This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this work enables efficient data processing of a large number of protein:ligand complexes, and provides better identification of specific protein properties that are responsible for a high probability of correct prediction of protein:ligand binding. The GPCR proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2), OPRD1 (Opioid receptor Delta 1) and OPRK1 (Opioid Receptor Kappa 1) are examined in this study using Big Data analytics techniques, which can efficiently process a huge ensemble of protein conformations, and significantly enhance the prediction of binding protein conformation  about 10–38 times better than its random selection counterpart for protein conformation selection. In addition to providing a Big Data approach to the conformational selection mechanism, this also opens the door to the systematic identification of such “binding conformations” for proteins. The physico-chemical features that are useful in predicting the “binding conformations” are largely, but not entirely, shared among the test proteins, indicating that the biophysical properties that drive the conformation selection mechanism may, to an extent, be protein-specific for the protein properties used in this work. The prediction of which small molecules, e.g., substrates or modulators, are more likely than other small molecules to bind to a specific protein, is one the most formidable challenges of contemporary biology, chemical biology and pharmacology. Only a small fraction of the large number of small organic molecules present in living organisms will, in most cases, bind to a specific protein. There is a considerable amount of work that aims at improving the biophysical approaches to predicting such protein:ligand interactions.As exemplified in the current special issue of Frontiers, the dynamics of the protein target is increasingly taken into account in such predictive approaches. Indeed, a protein cycles through multiple conformations, a few of which will be bound by its ligands, as conceptualized in the “conformational selection” mechanism of ligand binding. Virtual docking  that aimIn principle the “binding” protein conformations will correspond to the free energy minima of the (protein + ligand) complex free energy hypersurface. In our research, we are looking into whether we can identify these rare apo-conformations that possess this capacity to bind their ligands, while the vast majority of the other apo-protein conformations do not. This paper describes our Big Data analytics work toward such characterization of what properties of an apo-protein conformation more likely lead to conformational selection.The data we used here has been obtained using supermassive “ensemble docking” from proteins’ molecular dynamics simulations, and is described in . The datBig Data analytics provides an efficient approach to analyzing such a large amount of data, and also addresses the class imbalance problem , which iAs described in our previous work , MoleculADORA2A: This dataset has 50 attributes and consists of 2,998 protein conformations among which 851 protein conformations are “binding” and 2,147 protein conformations that are “non-binding”. Here the imbalance ratio is 3:1 i.e., for every datasample belonging to minority class (binding conformations) there are three data samples belonging to the majority class (non-binding conformations).ADRB2: This dataset has 51 attributes and consists of 2,565 protein conformations among which 156 are binding and 2,411 protein conformations are non-binding. Here the imbalance ratio is 16:1 i.e., for every datasample belonging to minority class (binding conformations) there are 16 data samples belonging to the majority class (non-binding conformations).OPRD1: This dataset has 51 attributes and consists of 3,004 protein conformations among which 72 protein conformations are binding and 2,932 protein conformations are non-binding. Here the imbalance ratio is 41:1 i.e., for every datasample belonging to minority class (binding conformations) there are 41 data samples belonging to the majority class (non-binding conformations).OPRK1: This dataset has 50 attributes and consists of 2,998 protein conformations among which 138 protein conformations are binding and 2,862 protein conformations are non-binding. Here the imbalance ratio is 20:1 i.e., for every data sample belonging to minority class (binding conformations) there are 20 data samples belonging to the majority class (non-binding conformations).Tables describing the protein attributes/features/descriptors for ADORA2A, ADRB2, OPRD1, and OPRK1 datasets can be found in our previous work . ADRB2 ax” features with the greatest F-values were selected in this case, where the x features to be retained is determined experimentally by the user. Thus, ANOVA technique allows for selection of the primary physio-chemical protein properties that essay a critical role in protein:ligand interaction and conformation selection.Analysis of variance (ANOVA) is a statistical analysis method used here to calculate the linear relationship between the various protein features and to select the important protein features that correspond to the highest F-values . The topU through the use of the other given random variable V. The mutual information I  for random variables U and V can be defined as follows  is the joint probability density function.• p(u) is the probability density functionMutual Information (MI)  is a mea follows :IU;V=−∑I is 1, then U and V are dependent on each other, i.e., protein features share similar information. If the MI value I is 0, then U and V are independent of each other i.e., no common (in other words unique) information between the features. The MI in physio-chemical properties are calculated as follows:• First, calculate the MI value for all properties to determine how dependent the physio-chemical features vectors are and understand the common information contained in all the protein features.x” protein features with the greatest MI values are retained, where x is user defined.• Then, sort the protein features according to their highest MI values. The top “In Eq. Recurrence Quantification Analysis (RQA) is a non-linear data-analysis method that is used to study the dynamical systems . The firx” highest rankings, where x is user-defined. Therefore, the Spearman correlation coefficient allows us to select protein features that are strongly correlated with each other.Spearman correlation coefficient is a statistical measure  of the sm samples and n protein descriptors, let (xi and corresponding outputs i (Extreme Gradient Boosting (XGBoost) is a tree ensemble boosting approach that merges a number of weak classifiers into a single strong classifier . Startints yi . The XGBfunction :ObjΘ=∑iK-Means clustering is an unsupervised machine learning algorithm  that is • • • u is the real input data• v is the noise input to the generator neural network• F(u) is the output probability of the generator• D(v) is the sample generated by the generator neural networkGenerative adversarial networks (GAN) is an unsupervised learning method that involves learning regularities or patterns in the input data to produce new examples that mimic the original dataset. The GAN technique uses two artificial models, the discriminator and generator, which compete for data learning . The disHere the GAN is used to oversample or replicate the minority class in the dataset to alleviate the class imbalance problem and in turn maximize the prediction of the potential binding protein conformations.Convolutional neural network (CNN) is a supervised deep learning technique  that hasThe architectural design of the CNN in our work consists of a convolutional layer followed by dropout to reduce overfitting, a max pooling layer, a fully connected layer and an output layer. Rectified linear unit (ReLU) is used as the activation function for the convolution layer and fully connected layer. Binary cross-entropy L is used as the loss function for the CNN.Recurrent neural network (RNN) is a class of neural networks which is used to detect patterns in a sequence of data . In our • True Positive (TP): When the classifier accurately predicts “binding,” indicating that the ligand and target protein did bind (Right predictions of class 1)• True Negative (TN): When the classifier accurately predicts “non-binding,” indicating that the ligand and target protein did not bind (Right predictions of class 0)• False Negative (FN): When the classifier inaccurately predicted “non-binding,” but the ligand and target protein did bind (Wrong predictions of class 0)• False Positive (FP): When the classifier inaccurately predicted “binding,” but the ligand and target protein did not bind (Wrong predictions of class 1)The confusion matrix and its derived evaluation parameters such as classification accuracy, sensitivity, specificity, etc., are some of the most commonly used ML evaluation metrics to validate a classification or prediction performance of ML algorithms. In this case of binary classification between binding and non-binding protein conformations, the confusion matrix has four categories of classification results as follows:Here class 0 refers to the non-binding protein conformations (majority class) and class 1 denotes the binding protein conformations (minority class).Accuracy of an AI/ML framework is calculated as the sum of correctly predicted binding and non-binding protein conformations divided by the total number of conformations in the data set. It is defined as:Sensitivity is the ability of the AI/ML framework to correctly predict binding protein conformations. It is calculated as the number of correctly predicted binding protein conformations divided by the total number of binding protein conformations in the data set as defined below:Eqs The enrichment was calculated using the TP and FN predictions from the Big Data analytics based AI/ML protein conformation selection/prediction framework, described in The final enrichment ratios for proteins ADORA2A, ADRB2, OPRD1, and OPRK1 were calculated using four different filters  and have been described and published in our previous work . The pro• The first step in the methodology is to input the dataset and then apply the ML feature selection methods: i) Analysis of variance (ANOVA), ii) Mutual Information (MI), iii) Recurrence Quantification Analysis (RQA), and iv) Spearman correlation to select the important protein features from each of the methods respectively.• We then obtain a feature ranking score for all features based on the common consensus of all the feature selection methods. Only the subset of protein features that are selected by all four feature selection methods are chosen to create a new dataset.• Both the original dataset and a new dataset that is more biased towards samples in class 0 are sent as inputs to the XGBoost classifier. Samples of class 0 (TN) and class 1 (TP) are recorded as classification results 1.• In order to create a new training dataset, the GAN algorithm was applied to both the original dataset and the new modified dataset.• K-Means clustering  is used • Supervised classification using deep learning methodologies: CNN and RNN are applied to the newly created training dataset. Both classifiers are used to identify the binding and non-binding conformations in the new training dataset. The results of both classifiers are recorded.• As a final step, the TP (binding conformations), and FN (binding conformations but are incorrectly predicted as non-binding conformations) by the AI/ML protein conformation prediction framework (CNN and RNN) are employed in the Enrichment ratio framework to calculate the Enrichment ratios. The outcomes of the framework for enrichment ratios are recorded.In this work, we combine the feature selection techniques discussed in The overview of enrichment ratios for ADORA2A that were determined using the predicted binding conformations from the AI/ML framework is shown in The list of protein descriptors that the four ML feature selection techniques determined to be significant is shown in The three common protein descriptors for the proteins ADORA2A, OPRK1, and OPRD1 that were determined to be significant by the four ML feature selection methods are listed in The overview of enrichment ratios for the ADRB2 binding conformations predicted by the AI/ML framework is shown in The list of protein descriptors that the three out of four ML feature selection techniques determined to be significant is shown in The summary of enrichment ratios for OPRD1 that were determined using the predicted binding conformations from the AI/ML framework is shown in The list of protein descriptors that the four ML feature selection techniques determined to be significant is shown in A summary of the enrichment ratios for OPRK1 that were determined using the predicted binding conformations from the AI/ML framework is shown in The list of protein descriptors that the four ML feature selection techniques determined to be significant is shown in An overview of the enrichment ratios that were calculated using the descriptors listed in pro_asa_vdw), the hydrophobic surface area (MOE descriptor pro_asa_hyd) and the hydrophobicity moment (MOE descriptor pro_hyd_moment). That these properties, which are global and not limited to the binding sites, are common to the important descriptor of all proteins point to a dual role of exposure to solvent and hydrophobicity as globally driving the capacity of proteins to bind, or not, their ligands. Note that this work is not a structure-activity relationship studies, i.e., we do not at this point give a range of values for these proteins that would be associated with ligand binding and a range of values that would be associated with non-ligand binding.The Big Data analytics research outcomes in this study suggest that four proteins ADORA2A, ADRB2, OPRK1, and OPRD1, and their binding conformations considered in this work do possess similar global properties that can be leveraged to predict whether they will be more likely to bind their ligands than other conformations. The enrichment factors obtained with the best approaches are about 10 to about 40 times better than what would be available with a random selection of protein conformations for docking. For three out of the four targets of interest here , the physico-chemical features that are most associated with a high propensity to be selected for binding by the ligands are the water accessible surface area  is conserved. We do not yet know if this difference observed between ADRB2 and the other proteins is a result of different actual physicochemical mechanisms involved in ligand binding, or if this is an artifact of the data and of specific issues with class imbalance from the MD trajectories of this target.Nonetheless, the fact that the apo-proteins’ global physico chemicals properties may—to an extent—predict the ligand-binding character of conformations is remarkable. Naturally, this does not mean that only global protein properties are “holding” the keys to the conformational selection mechanisms. This work will have to be continued and repeated with features that are specific to the binding sites’ conformations rather than describing the global protein structure.


Homo sapiens) and yeast (Saccharomyces cerevisiae) stress granule proteins. MaGS utilizes experimentally-based protein features for prediction, whereas MaGSeq is an entirely protein sequence-based implementation. GraPES is implemented in HTML/CSS and Javascript and is freely available for public use at https://grapes.msl.ubc.ca/. Documentation for using the provided webtools, descriptions of their methodology, and implementation notes can be found on the webpage.Phase separation-based condensate formation is a novel working paradigm in biology, helping to rationalize many important cellular phenomena including the assembly of membraneless organelles. Uncovering the functional impact of cellular condensates requires a better knowledge of these condensates’ constituents. Herein, we introduce the webserver GraPES (Granule Protein Enrichment Server), a user-friendly online interface containing the MaGS and MaGSeq predictors, which provide propensity scores for proteins’ localization into cellular condensates. Our webpage contains models trained on human (  Protein phase separation has been thrust to the forefront of molecular biology over the past decade . During Based on our findings and the availability of well-curated SG sets, we designed two predictors to score the propensity of proteins to localize to SGs. Ample testing revealed that these prediction scores were elevated for SG constituent proteins but also for those proteins which localize into other biological condensates. This finding was somewhat expected, given the biophysical mechanism of phase separation that is thought to underly condensate formation. Thus, by exploiting publicly available proteomic and database information, we introduced these two generalized predictors for protein localization into biological condensates, one for mammalian cells and one for yeast cells, called MaGS . These tools provide some of the highest known confidence predictions for biological condensate localization, outperforming similar computational methods .https://grapes.msl.ubc.ca/) users can look up a variety of pre-calculated propensity MaGS values for human and yeast proteins or obtain novel MaGSeq predictions from FASTA formatted protein sequences which, while these models have been optimized for mammalian or yeast condensate predictions, can in principle be used for any eukaryotic organism.Herein, we present a user-friendly, web-based interface called GraPES (Granule Protein Enrichment Server), which houses the original MaGS as well as the newly developed and complementary MaGSeq models. On the GraPES website , mouse (M. musculus), fruit fly (D. melanogaster), and thale cress . Proteins were taken from the ‘Scaffold’ and ‘Client’ classifications of the Cajal body, P-body, Stress granule, U-body, PcG body, Nuclear speckle, Nucleolus, and proteins in the ‘Other’ groups, where applicable, to construct ‘condensate protein’ data sets. Size-balanced negative controls were then constructed using randomly selected proteins not included in the drLLPS database.Data sets for condensate-specific comparisons for P-bodies, Cajal bodies, Nucleolus, PML-bodies, Nuclear speckles, Centrosome-Spindle pole bodies, and ications . Additiohttps://www.R-project.org/). We then removed features systematically and assessed model performance using Areas Under Curve (AUC) values of the Receiver Operating Characteristics (ROC) functions on the balanced positive and negative validation sets while monitoring feature contributions to the GLM fit. The features selected for the optimized MaGSeq models include: percent protein intrinsic disorder  for MaGSeq by following a standard protocol in which the model is optimized using training and validation sets and then benchmarked against a fully independent test set Figure . We initdisorder , π-π intdisorder , Soluprodisorder , RNA-bindisorder , GRAVY pdisorder , and thedisorder , Soluprodisorder , total sdisorder ; accordidisorder . Proteindisorder . After tdisorder . The selin vitro. However, it is thought that proteins which can form protein droplets in vitro are likely to act as ‘drivers’ in the formation of biological condensates within the cell. As the number of proteins that do not localize to SGs is significantly larger than the ones that do, we also calculated precision-recall (PR) curves using the complete negative test set not used in training or validation. Examination of these PR curves reveals that the MaGSeq models have a higher precision than the other methods at almost all levels of sensitivity , moderate (between 1.16 and 0.66 for human and between 1.08 and 0.58 for yeast) or low propensity (between 0.66 and −0.36 for human and between 0.58 and −0.39 for yeast) for cellular condensate inclusion. However, if no score is available, because experimental features used by MaGS are missing for the query protein, then MaGSeq can be used. It should be noted that scores from MaGS and MaGSeq are not directly comparable. Therefore, we provide different cutoff values for MaGSeq. Specifically, proteins with high, moderate and low propensity for cellular condensate localization are those human (yeast) proteins with scores > 0.90 (>0.89), between 0.90 and 0.56 (0.89 and 0.25), and between 0.56 and −0.33 (0.25 and −0.45), respectively.As a specific usage example, we show the results for Ubiquilin-2 (UBQLN2), a protein in our independent test set. It receives a MaGS score of 1.08, indicating that it has a moderate to high propensity to be associated with biological condensates, and indeed it has been noted to phase separate in a number of different conditions  and has A protein that is not contained in the MaGS database is LINE-1. This protein has recently been found to phase separate through its N-terminus and coiled-coil domain  and is aThe performance of MaGS is, as expected, higher than that of MaGSeq due to the model's use of experimental data. However, MaGS can only be applied to proteins for which protein abundance and annotated phosphorylation sites are known, while the MaGSeq models only use sequence information, thus providing flexibility to potentially query splice variants as well as protein sequences of other species. Indeed, MaGSeq predictors show significantly higher scores for known condensate proteins over randomized controls for a number of organisms . A closeAdditionally, while the MaGS and MaGSeq models were parameterized to predict stress granule proteins, these models identify proteins associated with many other biological condensates . This asS. cerevisiae is known to localize to stress granules and obtains a MaGS of 1.41 and a MaGSeq of 0.64, having a ‘high’ propensity in MaGS and a ‘moderate’ propensity in MaGSeq.MaGS and MaGSeq are complementary because they use different features to assess condensate localization and have different application ranges. Scores cannot be directly compared between the two models; however, more confidence can be gained if a protein obtains high scores across both models. If the models disagree in their predictions, then more weight should be given to the MaGS predictors as they are more accurate and account for biological features that go beyond what the primary sequence can provide. For instance, the protein Nab6 in In the analyses and validation of MaGS predictions , we founhttps://grapes.msl.ubc.ca/, the website source code is provided on Github https://github.com/JacobsonMT/GraPES, and source code for the GLM models and model data can be found at https://github.com/ekuec/2020_grapes_server for advanced users or those who wish to do high-throughput calculations using the MaGS or MaGSeq models.GraPES is an open-source web application designed to increase the usability and application of the MaGS and MaGSeq methods for the prediction of protein localization into biological condensates. This platform is deployed at gkac279_Supplemental_FilesClick here for additional data file.


The CYFIP2 protein (cytoplasmic FMR1-interacting protein 2) is part of the WAVE regulatory complex (WRC). CYFIP2 was recently correlated to neurological disorders by the association of the R87C variant with early infantile epileptic encephalopathy (EIEE) patients. In this set of syndromes, the epileptic spasms and seizures since early childhood lead to impaired neurological development in children. Inside the WRC, the variant residue is at the CYFIP2 and WAVE1 protein interface. Thus, the hypothesis is that the R87C modification weakens this interaction, allowing the WRC complex’s constant activation. This work aimed to investigate the impacts of the mutation on the structure of the WRC complex through molecular dynamics simulation. For that, we constructed WRC models containing WAVE1-NCKAP1 proteins complexed with WT or R87C CYFIP2. Our simulations showed a flexibilization of the loop comprising residues 80–110 due to the loss of contacts between internal residues in the R87C CYFIP2 as well as the key role of residues R/C87, E624, and E689 in structural modification. These data could explain the mechanism by which the mutation impairs the stability and proper regulation of the WRC. Early infantile epileptic encephalopathies (EIEE) are a rare group of neurodevelopment syndromes that appear in early childhood. In this pathology, convulsions and epileptic episodes compromise the proper neural development ,2. The pWith the recent advances in next-generation sequencing (NGS), single mutations in diverse genes have already been correlated with EIEE, such as CYFIP2 ,7. The CRecently, de novo mutations in residue R87 of CYFIP2 were associated with EIEE ,15,16,17Here, we were interested in investigating how these variants affect the structure and dynamics of CYFIP2. We therefore conducted molecular dynamics simulations of CYFIP2 within its WRC interactors and analyzed the effect of the mutations on the dynamic properties of CYFIP2. Our simulations establish a flexibilization in the loop 80–110 in the CYFIP2 R87C variant that could impair its own stability and interactions with WAVE protein. Furthermore, our results identified that the loss of contacts between residues R87, E624, and E689 could explain these structural modifications. Based on our models, these structural modifications might play an essential role in the stability and proper regulation of the WRC complex and, therefore, could help to explain these mutants’ phenotype. Moreover, our findings may provide information for future studies interested in the structure and dynamics of WRC complex mutants and possible ways to restore proper CYFIP2 function.The crystallographic structure of the WRC complex  shows a The backbone RMSDs from the initial models of the WT and R87C complex are displayed in Regarding the simulation of isolated CYFIP2 models, the RMSDs show that both the WT and R87C models reached equilibrium around 30 ns but with a higher RMSD for the WT model, which was between 0.7 and 0.9 nm. This implies that the CYFIP2 WT final structure differed more from the initial model than CYFIP2 R87C . Both st2 to a running average of about 1200 nm2 for the complexes (2 to 640 nm2 for the isolated CYFIP2 WT and R87C models (The radius of gyration (Rg) ranged between 6 and 6.2 nm during the entire simulation, considering both replicas and both WT and R87C complexes G. This iomplexes F and froC models . This maIn order to analyze the most relevant movements during the simulations, we performed a principal component analysis (PCA) for the complexes. In PCA, the samples are rewritten on the basis of the eigenvectors. The components of the formed vector are the principal components, and their variances are the eigenvalues , with thTo better analyze this change, we clustered the conformations explored by the complexes during the trajectory according to the RMSDs among their Cα atoms. A total of 5000 structures were divided into a maximum of 13 clusters, or families of structures, for the WT complex and 11 clusters for the R87C complex B. The suFinally, in an attempt to assess the flexibility of the R87C model compared to the WT model isolated and in the complex, we calculated the root mean square fluctuation (RMSF) for each CYFIP2 residue throughout the entire simulations A,C. For We hypothesized that this loop flexibilization could be caused by the loss of important interactions at residue 87 due to the mutation. In our initial model (not shown) and in the structure resulting from the complex trajectory clustering, residue 87 forms hydrogen bonds with CYFIP2 residues E624 and E689 and with WAVE1 residue Y151 A. These We also compared the position of Y151 with residues W86, S88, C89, and F686 from CYFIP2 and P131, P132, P133, L134, and L148 from WAVE1  because We then evaluated the behavior of these interactions throughout the trajectory, looking at the number of contacts between the residues A–D as weIn order to compare the CYFIP2 WT and the R87C isolated structures with the complex structures, the same analyses were conducted during the CYFIP2 WT and R87C simulations –F. In thMoreover, we evaluated the interactions of residue 87 with residue Y151 of WAVE1. Regarding these residues, we could see differences between the complexes and that the number of contacts varied more during the simulation. For the WT complex, the number of contacts varied between 150 and 250, with a minimum distance between 0.2 and 0.3 nm. For the R87C complex, the number of contacts varied between 50 and 150, with a minimum distance between 0.3 and 0.4 nm. An indication that, although there was a loss of contacts with the WAVE1 protein, the loss of internal contacts between CYFIP2 residues, causing the destabilization of the loop, seemed to be more relevant in our simulation time.Our molecular dynamics simulations demonstrated a movement of the loop where residue 87 of the protein resides. In the structure of both the isolated R87C CYFIP2 and the R87C complex, this loop was distant from the rest of the protein when compared with the WT complex and with the crystallographic X-ray structure of the WRC complex . ThroughIn the first studies to understand the modulation of the WRC complex, the consensus was that the complex disaggregates when interacting with Rac1-GTP and that this allowed the interaction of WAVE with the Arp2/3 complex . The revAs already noted in this and other studies ,17, resiThis disruption of the 80–110 loop structure could also impact CYFIP2 stability and its interaction with other proteins. Despite being more studied in the context of the WRC complex, CYFIP2 is also known for its interaction with other complexes and seems to participate in different cellular processes. Initially, CYFIP2 was identified and named PIR121 due to the induction of its gene by p53 . It was Therefore, the loop destabilization can impact protein function, not just in the WRC complex. However, this is difficult to verify by computer modeling because little is known about the exact interaction regions of CYFIP2 and these other proteins. In addition, experimental evidence shows a trend towards lower stability of CYFIP2 R87C but without conclusive results . In vitrThis work brings new in silico evidence on the possible structural alterations of the CYFIP2 protein resulting from the R87C modification, also present in patients with EIEE. This evidence seems to agree with the hypothesis of weakening binding with WAVE and with experimental results regarding the interaction of CYFIP2 R87C and WAVE1. Thus, through our simulations, we demonstrate the possible mechanism of structural modification associated with the pathological effects of the CYFIP2 R87C variant. The description of this behavior may guide further studies in the understanding of the molecular pathways and mechanisms compromised by CYFIP2 mutations. In addition, it could guide research in the development of a CYFIP2-targeted therapy.The alignment of the amino acid sequences of CYFIP2 and CYFIP1 was performed in clustal Ω , and theThe best model out of 100 generated tridimensional models was chosen based on the DOPE score . The comThe molecular dynamics (MD) of isolated CYFIP2 WT and CYFIP2 R87C were performed using GROMACS 2020.4  using thThe first step of energy minimization was performed using the steepest descent integrator  at 10,00After analyzing the structures obtained in isolated CYFIP2 simulations, we performed the complex simulations to investigate if the same pattern would be observed, even with the interaction between CYFIP2 and WAVE1. The simulation of CYFIP2 within the WRC complex was performed with GROMACS 2020.4  and a CHThe minimization using the steepest descent algorithm  was perfThe resultant trajectory of all simulations was processed using the gmx trjconv. The root-mean-square deviations (RMSD), root-mean-square fluctuations (RMSF), solvent-accessible surface areas (SASA), gyration radius (Rg), number of contacts, minimum distance between residues, and principal component analysis (PCA) were built using the GROMACS 2020.4 package . The traThe structure’s conformations were clustered with the g_cluster program in GROMACS 2020.4 using the Gromos algorithm . In orde