Protein Functional Sites Research Articles

Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.

De novo design of buttressed loops for sculpting protein functions

In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9–14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Identification of Evolutionary Trajectories Shared across Human Betacoronaviruses.

Comparing the evolution of distantly related viruses can provide insights into common adaptive processes related to shared ecological niches. Phylogenetic approaches, coupled with other molecular evolution tools, can help identify mutations informative on adaptation, although the structural contextualization of these to functional sites of proteins may help gain insight into their biological properties. Two zoonotic betacoronaviruses capable of sustained human-to-human transmission have caused pandemics in recent times (SARS-CoV-1 and SARS-CoV-2), although a third virus (MERS-CoV) is responsible for sporadic outbreaks linked to animal infections. Moreover, two other betacoronaviruses have circulated endemically in humans for decades (HKU1 and OC43). To search for evidence of adaptive convergence between established and emerging betacoronaviruses capable of sustained human-to-human transmission (HKU1, OC43, SARS-CoV-1, and SARS-CoV-2), we developed a methodological pipeline to classify shared nonsynonymous mutations as putatively denoting homoplasy (repeated mutations that do not share direct common ancestry) or stepwise evolution (sequential mutations leading towards a novel genotype). In parallel, we look for evidence of positive selection and draw upon protein structure data to identify potential biological implications. We find 30 candidate mutations, from which 4 (codon sites 18121 [nsp14/residue 28], 21623 [spike/21], 21635 [spike/25], and 23948 [spike/796]; SARS-CoV-2 genome numbering) further display evolution under positive selection and proximity to functional protein regions. Our findings shed light on potential mechanisms underlying betacoronavirus adaptation to the human host and pinpoint common mutational pathways that may occur during establishment of human endemicity.

An Activity-Based Oxaziridine Platform for Identifying and Developing Covalent Ligands for Functional Allosteric Methionine Sites: Redox-Dependent Inhibition of Cyclin-Dependent Kinase 4.

Activity-based protein profiling (ABPP) is a versatile strategy for identifying and characterizing functional protein sites and compounds for therapeutic development. However, the vast majority of ABPP methods for covalent drug discovery target highly nucleophilic amino acids such as cysteine or lysine. Here, we report a methionine-directed ABPP platform using Redox-Activated Chemical Tagging (ReACT), which leverages a biomimetic oxidative ligation strategy for selective methionine modification. Application of ReACT to oncoprotein cyclin-dependent kinase 4 (CDK4) as a representative high-value drug target identified three new ligandable methionine sites. We then synthesized a methionine-targeting covalent ligand library bearing a diverse array of heterocyclic, heteroatom, and stereochemically rich substituents. ABPP screening of this focused library identified 1oxF11 as a covalent modifier of CDK4 at an allosteric M169 site. This compound inhibited kinase activity in a dose-dependent manner on purified protein and in breast cancer cells. Further investigation of 1oxF11 found prominent cation-π and H-bonding interactions stabilizing the binding of this fragment at the M169 site. Quantitative mass-spectrometry studies validated 1oxF11 ligation of CDK4 in breast cancer cell lysates. Further biochemical analyses revealed cross-talk between M169 oxidation and T172 phosphorylation, where M169 oxidation prevented phosphorylation of the activating T172 site on CDK4 and blocked cell cycle progression. By identifying a new mechanism for allosteric methionine redox regulation on CDK4 and developing a unique modality for its therapeutic intervention, this work showcases a generalizable platform that provides a starting point for engaging in broader chemoproteomics and protein ligand discovery efforts to find and target previously undruggable methionine sites.

Scaffolding protein functional sites using deep learning.

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

Structural Biology Meets Biomolecular Networks: The Post-AlphaFold Era

Background: Recent progress in protein structure prediction by AlphaFold has opened new avenues to decipher biological functions from the perspective of structural biology based on the proteomics level. Methods: To meet these challenges, in this perspective, three scales of networks for protein structures, including structural protein-protein networks, protein structural networks, and elastic network models were introduced for high-throughput modeling of protein functional sites and protein dynamics. Conclusion: In the post-AlphaFold era, it is assumed that the integration of biomolecular networks may be leveraged in the future to develop a modeling framework that addresses protein structure-based functions with the application in drug discovery.

Capturing the geometry, function, and evolution of enzymes with 3D templates.

Structural templates are 3D signatures representing protein functional sites, such as ligand binding cavities, metal coordination motifs, or catalytic sites. Here we explore methods to generate template libraries and algorithms to query structures for conserved 3D motifs. Applications of templates are discussed, as well as some exemplar cases for examining evolutionary links in enzymes. We also introduce the concept of using more than one template per structure to represent flexible sites, as an approach to better understand catalysis through snapshots captured in enzyme structures. Functional annotation from structure is an important topic that has recently resurfaced due to the new more accurate methods of protein structure prediction. Therefore, we anticipate that template‐based functional site detection will be a powerful tool in the task of characterizing a vast number of new protein models.

HGG-54. CLK1 aberrant splicing in pediatric high-grade gliomas disrupts key oncogenic transcriptional programs

Abstract While much of the somatic coding variation underlying the oncogenic transformation of pediatric high-grade gliomas (HGGs) has been profiled, transcriptional splicing programs of these tumors remain under-explored. Here, we characterize aberrant alternative splicing in pediatric midline HGGs (n = 84). We identified 19,275 recurrent and significant (20% change from control, P < 0.05, FDR < 0.05) aberrant splicing events in 8,587 genes compared to non-diseased brainstem controls. Of those, 27% (n = 5,157) resulted in either a gain or loss of a known protein functional site within 3,294 genes. We prioritized splice variants affecting targetable kinases and found that mRNAs encoding CDC-like kinase 1 (CLK1), a known modulator of master splicing regulators, exhibit significantly increased exon 4 inclusion in midline HGGs. This leads to a gain of two known phosphorylation sites in CLK1, increased CLK1 protein expression and hyper-phosphorylation of Serine-rich splicing factors. To assess the impact of this event, we performed differential splicing and expression analyses, comparing tumors with the highest (n= 5) and lowest (n = 5) exon 4 inclusion. We discovered 3,037 genes to be differentially up-regulated in high exon 4 inclusion tumors with an enrichment of cancer-related pathways, including DNA repair, mitotic spindle, myogenesis and EMT. We next integrated these gene signatures with protein-protein interaction networks of kinase and transcription factors and show that increased CLK1 exon 4 inclusion disrupts critical regulatory networks, such as those involving FOXM1, which is implicated in cell cycle and proliferation processes. In summary, we describe aberrant splicing in pediatric HGGs as an additional mechanism that could drive tumorigenesis. Future work will focus on molecular validation and therapeutic targeting of CLK1 in available HGG models. Characterizing tumor-specific splicing variation has the potential to open new therapeutic strategies and understand mechanisms of treatment resistance in children with central nervous system tumors.

Gene expression and transcriptional regulation driven by transcription factors involved in congenital heart defects.

Congenital heart disease (CHD) is one of the most important birth defects caused by more than one mutated gene. Mutations in the genes could cause different types of congenital heart defects including atrial septal defect (ASD), tetralogy of Fallot (TOF), and ventricular septal defect (VSD). Cardiac transcription factors are key players for heart development and are actively involved in controlling stress regulation of the heart. Transcription factors are sequence-specific DNA binding proteins that control the process of transcription and work in a synergistic manner. We aim to characterize core cardiac transcription factors including NKX2-5, TBX, SRF, GATA4, and MEF2, which encode homeobox and MADS domain and play a crucial role in heart development. In this study, we have explored the important transcription factors involved in cardiac development and genes controlling the expression and regulation process by using the bioinformatics approach. We have predicted the orthologs and homologs based on their evolutionary history, conserved protein domains, functional sites, and 3D structures for better understanding and presentation of factors responsible for causing CHD. Results showed the importance of these transcription factors for normal heart functioning and development. Understanding the molecular pathways and genetic basis of CHD will help to open a new door for the treatment of patients with cardiac defects.

Identifying vaccine escape sites via statistical comparisons of short-term molecular dynamics

The identification of viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a molecular dynamics (MD)-based approach that goes beyond contact mapping, scales well to a desktop computer with a modern graphics processor, and enables the user to identify functional protein sites that are prone to vaccine escape in a viral antigen. We first implement our MD pipeline to employ site-wise calculation of Kullback-Leibler divergence in atom fluctuation over replicate sets of short-term MD production runs thus enabling a statistical comparison of the rapid motion of influenza hemagglutinin (HA) in both the presence and absence of three well-known neutralizing antibodies. Using this simple comparative method applied to motions of viral proteins, we successfully identified in silico all previously empirically confirmed sites of escape in influenza HA, predetermined via selection experiments and neutralization assays. Upon the validation of our computational approach, we then surveyed potential hotspot residues in the receptor binding domain of the SARS-CoV-2 virus in the presence of COVOX-222 and S2H97 antibodies. We identified many single sites in the antigen-antibody interface that are similarly prone to potential antibody escape and that match many of the known sites of mutations arising in the SARS-CoV-2 variants of concern. In the Omicron variant, we find only minimal adaptive evolutionary shifts in the functional binding profiles of both antibodies. In summary, we provide an inexpensive and accurate computational method to monitor hotspots of functional evolution in antibody binding footprints.

RosettaSurf-A surface-centric computational design approach.

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

Interaction between MAPKs and MKPs in hexaploid chrysanthemum illuminates functional paralogue diversification in polyploids

Mitogen-activated protein kinases (MAPKs, also known as MPKs) regulate diverse cellular and physiological functions, and dual-specificity MAPK phosphatases (MKPs) modulate MAPK signalling through MAPK dephosphorylation and inactivation. Due to lacking of overall understanding for the regulatory networks between Chrysanthemum morifolium MKPs (CmMKPs) and C. morifolium MAPKs (CmMPKs), we systematically studied the interactions between four groups of CmMPKs and eight identified CmMKPs in chrysanthemum and found that the interaction between the specific CmMKP and the specific CmMPK differed from those in other plants. Furthermore, the expression of CmMKP1 and CmMKP1-LIKE1 showed opposite trends during the development of chrysanthemum flower buds under salt treatment and Alternaria alternata inoculation, but these genes could interact with the same CmMPKs, providing insight into the subfunctionalization of paralogues. Amino acid variations (M87V, T277P and V6L) in dual-specificity protein phosphatases (DsPTP1)-LIKE1/2/3 changed the interactions of these proteins with the four CmMPK groups in chrysanthemum, providing evidence for the de/neofunctionalization of paralogues in polyploids, suggesting that we can identify the key functional sites of proteins by studying polyploid paralogues.

Quantitative Chemoproteomic Profiling with Data-Independent Acquisition-Based Mass Spectrometry.

Activity-based protein profiling (ABPP) has emerged as a powerful and versatile tool to enable annotation of protein functions and discovery of targets of bioactive ligands in complex biological systems. It utilizes chemical probes to covalently label functional sites in proteins so that they can be enriched for mass spectrometry (MS)-based quantitative proteomics analysis. However, the semistochastic nature of data-dependent acquisition and high cost associated with isotopically encoded quantification reagents compromise the power of ABPP in multidimensional analysis and high-throughput screening, when a large number of samples need to be quantified in parallel. Here, we combine the data-independent acquisition (DIA) MS with ABPP to develop an efficient label-free quantitative chemical proteomic method, DIA-ABPP, with good reproducibility and high accuracy for high-throughput quantification. We demonstrated the power of DIA-ABPP for comprehensive profiling of functional cysteineome in three distinct applications, including dose-dependent quantification of cysteines' sensitivity toward a reactive metabolite, screening of ligandable cysteines with a covalent fragment library, and profiling of cysteinome fluctuation in circadian clock cycles. DIA-ABPP will open new opportunities for in-depth and multidimensional profiling of functional proteomes and interactions with bioactive small molecules in complex biological systems.

Computational prediction of protein functional sites-Applications in biotechnology and biomedicine.

There are many computational approaches for predicting protein functional sites based on different sequence and structural features. These methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. They complement the more expensive and time-consuming experimental approaches by pointing them to possible candidate positions. In many cases they are jointly used to characterize the functional sites in proteins of biotechnological and biomedical interest and eventually modify them for different purposes. There is a clear trend towards approaches based on machine learning and those using structural information, due to the recent developments in these areas. Nevertheless, "classic" methods based on sequence and evolutionary features are still playing an important role as these features are strongly related to functionality. In this review, the main approaches for predicting general functional sites in a protein are discussed, with a focus on sequence-based approaches.

A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing.

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational “anchor extension” methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

CAGE-prox: A Unified Approach for Time-Resolved Protein Activation in Living Systems.

Temporal activation of proteins of interest (POIs) offers a gain-of-function approach to investigate protein functions in dynamic biological processes. Fusion of photo/chemical-switchable proteins to a POI, or site-specific blockage/decaging of catalytic residue(s) on a POI, are the most widely utilized strategies for selective protein activation. These methods, however, either lack generality (e.g., active site decaging) or would modify the POI with a bulky tag (e.g., genetic fusion). Recently, a computationally aided and genetically encoded proximal decaging strategy (CAGE-prox) has been developed for time-resolved photoactivation of a broad range of proteins in living systems. In contrast to the direct decaging of the active site of a POI, CAGE-prox relies on a unified caged amino acid that can be anchored in proximity to a protein's functional site for temporal blockage of its activity until rescued by photo/chemical decaging. In order to identify the optimal site for photo-caged unnatural amino acid insertion, which is key for the effective blockade and re-activation of the POI, a computational algorithm was developed to screen all possible positions in close proximity to the functional site that would enable turning off/on protein activity via caging/decaging operations. Here, we describe the CAGE-prox strategy, from in silico design to experimental validation, and provide various examples of its application. © 2021 Wiley Periodicals LLC Basic Protocol 1: In silico design and experimental validation of CAGE-prox Basic Protocol 2: Orthogonal activation of a POI by CAGE-prox while minimizing the activity from the endogenous protein Basic Protocol 3: CAGE-prox-enabled, time-resolved proteomics for the identification of substrates of a proteolytic enzyme Basic Protocol 4: Controlled activation of protein-based prodrugs for tumor therapy.

Quantifying evolutionary importance of protein sites: A Tale of two measures.

A key challenge in evolutionary biology is the accurate quantification of selective pressure on proteins and other biological macromolecules at single-site resolution. The evolutionary importance of a protein site under purifying selection is typically measured by the degree of conservation of the protein site itself. A possible alternative measure is the strength of the site-induced conservation gradient in the rest of the protein structure. However, the quantitative relationship between these two measures remains unknown. Here, we show that despite major differences, there is a strong linear relationship between the two measures such that more conserved protein sites also induce stronger conservation gradient in the rest of the protein. This linear relationship is universal as it holds for different types of proteins and functional sites in proteins. Our results show that the strong selective pressure acting on the functional site in general percolates through the rest of the protein via residue-residue contacts. Surprisingly however, catalytic sites in enzymes are the principal exception to this rule. Catalytic sites induce significantly stronger conservation gradients in the rest of the protein than expected from the degree of conservation of the site alone. The unique requirement for the active site to selectively stabilize the transition state of the catalyzed chemical reaction imposes additional selective constraints on the rest of the enzyme.

Biological impact of mutually exclusive exon switching.

Alternative splicing can expand the diversity of proteomes. Homologous mutually exclusive exons (MXEs) originate from the same ancestral exon and result in polypeptides with similar structural properties but altered sequence. Why would some genes switch homologous exons and what are their biological impact? Here, we analyse the extent of sequence, structural and functional variability in MXEs and report the first large scale, structure-based analysis of the biological impact of MXE events from different genomes. MXE-specific residues tend to map to single domains, are highly enriched in surface exposed residues and cluster at or near protein functional sites. Thus, MXE events are likely to maintain the protein fold, but alter specificity and selectivity of protein function. This comprehensive resource of MXE events and their annotations is available at: http://gene3d.biochem.ucl.ac.uk/mxemod/. These findings highlight how small, but significant changes at critical positions on a protein surface are exploited in evolution to alter function.