A Competitive High-Throughput Screening Platform for Designing Polylactic Acid-Specific Binding Peptides

Among biobased polymers, polylactic acid (PLA) is recognized as one of the most promising bioplastics to replace petrochemical-based polymers. PLA is typically blended with other polymers such as polypropylene (PP) for improved melt processability, thermal stability, and stiffness. A technical challenge in recycling of PLA/PP blends is the sorting/separation of PLA from PP. Material binding peptides (MBPs) can bind to various materials. Engineered MBPs that can bind in a material-specific manner have a high potential for material-specific detection or enhanced degradation of PLA in mixed PLA/PP plastics. To obtain a material-specific MBP for PLA binding (termed PLAbodies ), protein engineering of MBP Cg-Def for improved PLA binding specificity is reported in this work. In detail, a 96-well microtiter plate based high-throughput screening system for PLA specific binding (PLABS) was developed and validated in a protein engineering (KnowVolution) campaign. Finally, the Cg-Def variant V2 (Cg-Def S19K/K10L/N13H) with a 2.3-fold improved PLA binding specificity compared to PP was obtained. Contact angle and surface plasmon resonance measurements confirmed improved material-specific binding of V2 to PLA (1.30-fold improved PLA surface coverage). The established PLABS screening platform represents a general methodology for designing PLAbodies for applications in detection, sorting, and material-specific degradation of PLA in mixed plastics.

The architecture of binding cooperativity between densely bound transcription factors

The binding of transcription factors (TFs) along genomes is restricted to a subset of sites containing their preferred motifs. TF-binding specificity is often attributed to the co-binding of interacting TFs; however, apart from specific examples, this model remains untested. Here, we define dependencies among budding yeast TFs that localize to overlapping promoters by profiling the genome-wide consequences of co-depleting multiple TFs. We describe unidirectional interactions, revealing Msn2 as a central factor allowing TF binding at its target promoters. By contrast, no case of mutual cooperation was observed. Particularly, Msn2 retained binding at its preferred promoters upon co-depletion of fourteen similarly bound TFs. Overall, the consequences of TF co-depletions were moderate, limited to a subset of promoters, and failed to explain the role of regions outside the DNA-binding domain in directing TF-binding preferences. Our results call for re-evaluating the role of cooperative interactions in directing TF-binding preferences.

Determining DNA-Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA-protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein-nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA-protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA-protein interactions and their apparent affinities in various biological and pathological contexts.

Designed loop extension followed by combinatorial screening confers high specificity to a broad matrix metalloproteinaseinhibitor

Matrix metalloproteinases (MMPs) are key drivers of various diseases, including cancer. Development of selective probes and drugs capable of selectively inhibiting the individual members of the large MMP family remains a persistent challenge. The inhibitory N-terminal domain of tissue inhibitor of metalloproteinases-2 (N-TIMP2), a natural broad MMP inhibitor, can provide a scaffold for protein engineering to create more selective MMP inhibitors. Here, we pursued a unique approach harnessing both computational design and combinatorial screening to confer high binding specificity toward a target MMP in preference to an anti-target MMP. We designed a loop extension of N-TIMP2 to allow new interactions with the non-conserved MMP surface and generated an efficient focused library for yeast surface display, which was then screened for high binding to the target MMP-14 and low binding to anti-target MMP-3. Deep sequencing analysis identified the most promising variants, which were expressed, purified, and tested for selectivity of inhibition. Our best N-TIMP2 variant exhibited 29 pM binding affinity to MMP-14 and 2.4 µM affinity to MMP-3, revealing 7500-fold greater specificity than WT N-TIMP2. High-confidence structural models were obtained by including NGS data in the AlphaFold multiple sequence alignment. The modeling together with experimental mutagenesis validated our design predictions, demonstrating that the loop extension packs tightly against non-conserved residues on MMP-14 and clashes with MMP-3. This study demonstrates how introduction of loop extensions in a manner guided by target protein conservation data and loop design can offer an attractive strategy to achieve specificity in design of protein ligands.

Physicochemical models of protein-DNA binding with standard and modified base pairs

DNA-binding proteins play important roles in various cellular processes, but the mechanisms by which proteins recognize genomic target sites remain incompletely understood. Functional groups at the edges of the base pairs (bp) exposed in the DNA grooves represent physicochemical signatures. As these signatures enable proteins to form specific contacts between protein residues and bp, their study can provide mechanistic insights into protein-DNA binding. Existing experimental methods, such as X-ray crystallography, can reveal such mechanisms based on physicochemical interactions between proteins and their DNA target sites. However, the low throughput of structural biology methods limits mechanistic insights for selection of many genomic sites. High-throughput binding assays enable prediction of potential target sites by determining relative binding affinities of a protein to massive numbers of DNA sequences. Many currently available computational methods are based on the sequence of standard Watson-Crick bp. They assume that the contribution of overall binding affinity is independent for each base pair, or alternatively include dinucleotides or short k-mers. These methods cannot directly expand to physicochemical contacts, and they are not suitable to apply to DNA modifications or non-Watson-Crick bp. These variations include DNA methylation, and synthetic or mismatched bp. The proposed method, DeepRec, can predict relative binding affinities as function of physicochemical signatures and the effect of DNA methylation or other chemical modifications on binding. Sequence-based modeling methods are in comparison a coarse-grain description and cannot achieve such insights. Our chemistry-based modeling framework provides a path towards understanding genome function at a mechanistic level.

Utilizing genetic code expansion to modify N-TIMP2 specificity towards MMP-2, MMP-9, and MMP-14

Matrix metalloproteinases (MMPs) regulate the degradation of extracellular matrix (ECM) components in biological processes. MMP activity is controlled by natural tissue inhibitors of metalloproteinases (TIMPs) that non-selectively inhibit the function of multiple MMPs via interaction with the MMPs' Zn ²⁺ -containing catalytic pocket. Recent studies suggest that TIMPs engineered to confer MMP specificity could be exploited for therapeutic purposes, but obtaining specific TIMP-2 inhibitors has proved to be challenging. Here, in an effort to improve MMP specificity, we incorporated the metal-binding non-canonical amino acids (NCAAs), 3,4-dihydroxyphenylalanine (L-DOPA) and (8-hydroxyquinolin-3-yl)alanine (HqAla), into the MMP-inhibitory N-terminal domain of TIMP2 (N-TIMP2) at selected positions that interact with the catalytic Zn ²⁺ ion (S2, S69, A70, L100) or with a structural Ca ²⁺ ion (Y36). Evaluation of the inhibitory potency of the NCAA-containing variants towards MMP-2, MMP-9 and MMP-14 in vitro revealed that most showed a significant loss of inhibitory activity towards MMP-14, but not towards MMP-2 and MMP-9, resulting in increased specificity towards the latter proteases. Substitutions at S69 conferred the best improvement in selectivity for both L-DOPA and HqAla variants. Molecular modeling revealed how MMP-2 and MMP-9 are better able to accommodate the bulky NCAA substituents at the intermolecular interface with N-TIMP2. The models also showed that, rather than coordinating to Zn ²⁺ , the NCAA side chains formed stabilizing polar interactions at the intermolecular interface with MMP-2 and MMP-9. The findings illustrate how incorporation of NCAAs can be used to probe and exploit differential tolerance for substitution within closely related protein-protein complexes to achieve improved specificity.

Structure-based prediction of T cell receptor:peptide-MHC interactions

The regulatory and effector functions of T cells are initiated by the binding of their cell-surface T cell receptor (TCR) to peptides presented by major histocompatibility complex (MHC) proteins on other cells. The specificity of TCR:peptide-MHC interactions, thus, underlies nearly all adaptive immune responses. Despite intense interest, generalizable predictive models of TCR:peptide-MHC specificity remain out of reach; two key barriers are the diversity of TCR recognition modes and the paucity of training data. Inspired by recent breakthroughs in protein structure prediction achieved by deep neural networks, we evaluated structural modeling as a potential avenue for prediction of TCR epitope specificity. We show that a specialized version of the neural network predictor AlphaFold can generate models of TCR:peptide-MHC interactions that can be used to discriminate correct from incorrect peptide epitopes with substantial accuracy. Although much work remains to be done for these predictions to have widespread practical utility, we are optimistic that deep learning-based structural modeling represents a path to generalizable prediction of TCR:peptide-MHC interaction specificity.

Molecular determinants of TRAF6 binding specificity suggest that native interaction partners are not optimized for affinity

TRAF6 is an adaptor protein involved in signaling pathways that are essential for development and the immune system. It participates in many protein-protein interactions, some of which are mediated by the C-terminal MATH domain, which binds to short peptide segments containing the motif PxExx[FYWHDE], where x is any amino acid. Blocking MATH domain interactions is associated with favorable effects in various disease models. To better define TRAF6 MATH domain binding preferences, we screened a combinatorial library using bacterial cell-surface peptide display. We identified 236 of the best TRAF6-interacting peptides and a set of 1,200 peptides that match the sequence PxE but do not bind TRAF6 MATH. The peptides that were most enriched in the screen bound TRAF6 tighter than previously measured native peptides. To better understand the structural basis for TRAF6 interaction preferences, we built all-atom structural models of the MATH domain in complex with high-affinity binders and nonbinders identified in the screen. We identified favorable interactions for motif features in binders as well as negative design elements distributed across the motif that can disfavor or preclude binding. Searching the human proteome revealed that the most biologically relevant TRAF6 motif matches occupy a different sequence space from the best hits discovered in combinatorial library screening, suggesting that native interactions are not optimized for affinity. Our experimentally determined binding preferences and structural models support the design of peptide-based interaction inhibitors with higher affinities than endogenous TRAF6 ligands.

Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches

Single-stranded DNA (ssDNA) binding proteins (SSBs) are critical in maintaining genome stability by protecting the transient existence of ssDNA from damage during essential biological processes, such as DNA replication and gene transcription. The single-stranded region of telomeres also requires protection by ssDNA binding proteins from being attacked in case it is wrongly recognized as an anomaly. In addition to their critical roles in genome stability and integrity, it has been demonstrated that ssDNA and SSB-ssDNA interactions play critical roles in transcriptional regulation in all three domains of life and viruses. In this review, we present our current knowledge of the structure and function of SSBs and the structural features for SSB binding specificity. We then discuss the machine learning-based approaches that have been developed for the prediction of SSBs from double-stranded DNA (dsDNA) binding proteins (DSBs).

Partitioned usage of chromatin remodelers by nucleosome-displacing factors

Nucleosome-displacing-factors (NDFs) in yeast, similar to pioneer factors in higher eukaryotes, can open closed chromatin and generate nucleosome-depleted regions (NDRs). NDRs in yeast are also affected by ATP-dependent chromatin remodelers (CRs). However, how NDFs and CRs coordinate in nucleosome invasion and NDR formation is still unclear. Here, we design a high-throughput method to systematically study the interplay between NDFs and CRs. By combining an integrated synthetic oligonucleotide library with DNA methyltransferase-based, single-molecule nucleosome mapping, we measure the impact of CRs on NDRs generated by individual NDFs. We find that CRs are dispensable for nucleosome invasion by NDFs, and they function downstream of NDF binding to modulate the NDR length. A few CRs show high specificity toward certain NDFs; however, in most cases, CRs are recruited in a factor-nonspecific and NDR length-dependent manner. Overall, our study provides a framework to investigate how NDFs and CRs cooperate to regulate chromatin opening.

Chromatin accessibility in yeast is regulated by nucleosome-displacing-factors (NDFs) and chromatin remodelers (CRs). Chen et al. show that NDFs first invade into nucleosomes and then recruit CRs to modulate the NDR length. NDF-specific and NDR length-dependent recruitment of CRs allow partitioned usage of CRs by NDFs.

coli cytoplasm

Sensitive and specific analysis of isomiroestrol (Iso) is required for the quality control of Pueraria candollei, an herb used to treat menopausal disorders. The anti-isomiroestrol monoclonal antibody (Iso-mAb) exhibits cross-reactivity with miroestrol and deoxymiroestrol, which impacts the analytical results. Here, the active and soluble forms of the single-chain variable fragment (Iso-scFv) and fragment antigen-binding (Iso-Fab) against Iso were expressed using Escherichia coli SHuffle® T7 to alter the binding specificity. The Iso-scFv format exhibited a higher binding activity than the Iso-Fab format. The reactivity of Iso-scFv towards Iso was comparable to that of the parental Iso-mAb. Remarkably, the binding specificity of the scFv structure was improved and cross-reactivity against analogs was reduced from 13.3-21.0% to less than 1%. The structure of recombinant antibodies affects the binding characteristics. Therefore, the immunoassays should improve specificity; these findings can be useful in agricultural processes and for quality monitoring of P. candollei-related materials.

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses

Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.

Engineering of tissue inhibitor of metalloproteinases TIMP-1 for fine discrimination between closely-related stromelysins MMP-3 and MMP-10

Matrix metalloproteinases (MMPs) have long been known as key drivers in the development and progression of diseases, including cancer and neurodegenerative, cardiovascular, and many other inflammatory and degenerative diseases, making them attractive potential drug targets. Engineering selective inhibitors based upon tissue inhibitors of metalloproteinases (TIMPs), endogenous human proteins that tightly yet nonspecifically bind to the family of MMPs, represents a promising new avenue for therapeutic development. Here, we used a counter-selective screening strategy for directed evolution of yeast-displayed human TIMP-1 to obtain TIMP-1 variants highly selective for the inhibition of MMP-3 in preference over MMP-10. As MMP-3 and MMP-10 are the most similar MMPs in sequence, structure, and function, our results thus clearly demonstrate the capability for engineering full-length TIMP proteins to be highly selective MMP inhibitors. We show using protein crystal structures and models of MMP-3-selective TIMP-1 variants bound to MMP-3 and counter-target MMP-10 how structural alterations within the N-terminal and C-terminal TIMP-1 domains create new favorable and selective interactions with MMP-3 and disrupt unique interactions with MMP-10. While our MMP-3-selective inhibitors may be of interest for future investigation in diseases where this enzyme drives pathology, our platform and screening strategy can be employed for developing selective inhibitors of additional MMPs implicated as therapeutic targets in disease.

An Evolutionary Perspective on Hox Binding Site Preferences in Two Different Tissues

Transcription factor (TF) networks define the precise development of multicellular organisms. While many studies focused on TFs expressed in specific cell types to elucidate their contribution to cell specification and differentiation, it is less understood how broadly expressed TFs perform their precise functions in the different cellular contexts. To uncover differences that could explain tissue-specific functions of such TFs, we analyzed here genomic chromatin interactions of the broadly expressed Drosophila Hox TF Ultrabithorax (Ubx) in the mesodermal and neuronal tissues using bioinformatics. Our investigations showed that Ubx preferentially interacts with multiple yet tissue-specific chromatin sites in putative regulatory regions of genes in both tissues. Importantly, we found the classical Hox/Ubx DNA binding motif to be enriched only among the neuronal Ubx chromatin interactions, whereas a novel Ubx-like motif with rather low predicted Hox affinities was identified among the regions bound by Ubx in the mesoderm. Finally, our analysis revealed that tissues-specific Ubx chromatin sites are also different with regards to the distribution of active and repressive histone marks. Based on our data, we propose that the tissue-related differences in Ubx binding behavior could be a result of the emergence of the mesoderm as a new germ layer in triploblastic animals, which might have required the Hox TFs to relax their binding specificity.

A Thermodynamic Analysis of the Binding Specificity between Four Human PDZ Domains and Eight Host, Viral and Designed Ligands

PDZ domains are binding modules mostly involved in cell signaling and cell–cell junctions. These domains are able to recognize a wide variety of natural targets and, among the PDZ partners, viruses have been discovered to interact with their host via a PDZ domain. With such an array of relevant and diverse interactions, PDZ binding specificity has been thoroughly studied and a traditional classification has grouped PDZ domains in three major specificity classes. In this work, we have selected four human PDZ domains covering the three canonical specificity-class binding mode and a set of their corresponding binders, including host/natural, viral and designed PDZ motifs. Through calorimetric techniques, we have covered the entire cross interactions between the selected PDZ domains and partners. The results indicate a rather basic specificity in each PDZ domain, with two of the domains that bind their cognate and some non-cognate ligands and the two other domains that basically bind their cognate partners. On the other hand, the host partners mostly bind their corresponding PDZ domain and, interestingly, the viral ligands are able to bind most of the studied PDZ domains, even those not previously described. Some viruses may have evolved to use of the ability of the PDZ fold to bind multiple targets, with resulting affinities for the virus–host interactions that are, in some cases, higher than for host–host interactions.

Pepscan Approach for the Identification of Protein-Protein Interfaces: Lessons from Experiment

PEPscan is an old approach that has recently gained renewed interest for the identification of interfering peptides (IPs), i.e., peptides able to interfere with protein-protein interactions (PPIs). Its principle is to slice a protein sequence as a series of short overlapping peptides that are synthesized on a peptide array and tested for their ability to bind a partner, with positive spots corresponding to candidate IPs. PEPscan has been applied with a rather large success in various contexts, but the structural determinants underlying this success remain obscure. Here, we analyze the results of 14 PEPscan experiments, and confront the in vitro results with the available structural information. PEPscan identifies candidate IPs in limited numbers that in all cases correspond to solvent-accessible regions of the structures, their location at the protein-protein interface remaining to be further demonstrated. A strong point of PEPscan seems to be its ability to identify specific IPs. IPs identified from the same protein differ depending on the target PPI, and correspond to patches not frequently involved in the interactions seen in the 3D structures available. Overall, PEPscan seems to provide a cheap and rapid manner to identify candidate IPs, that also comes with room for improvement.

Time-dependent conformational analysis of ALK5-lumican complex in presence of graphene and graphene oxide employing molecular dynamics and MMPBSA calculation

Lumican, an extracellular matrix protein avails wound healing by binding to ALK5 membrane receptor (TGF-beta receptor I). Their interaction enables epithelialization and substantiates rejuvenation of injured tissue. To enrich permanence of ALK5-lumican interaction, we employed graphene and graphene oxide co-factors. Herein, this study explicates concomitancy of graphene and graphene oxide with ALK5-lumican. We performed an in silico approach involving molecular modelling, molecular docking, molecular dynamics for 200 ns, DSSP analysis and MMPBSA calculations. Results of molecular dynamics indicate cofactors influential in altering bioactive site of lumican than ALK5. Similarly, MMPBSA calculations unveiled binding energy of apoenzyme as -108.09 kcal/mol, holoenzyme (G) as -79.20 kcal/mol and holoenzyme (GO) as -114.33 kcal/mol. This concludes graphene oxide lucrative in enhancing binding energy of ALK5-lumican in holoenzyme (GO) via coil formation of Lum C₁₃ domain. In contrast, graphene reduced binding energy of ALK5-lumican in holoenzyme (G) modifying Lum C₁₃ into beta sheets. MMPBSA residual contribution analysis of Lum C₁₃ residues revealed binding energy of -13.9 kcal/mol for apoenzyme, -6.8 kcal/mol for holoenzyme (G) and -19.5 kcal/mol for holoenzyme (GO). This supports coil formation propitious for better ALK5-Lum interaction. Highest SASA energy of -21.05 kcal/mol of holoenzyme (G) assures graphene reasonable for improved ALK5-lumican hydrophobicity. As per the motive of the study, graphene oxide enriches permanence of ALK5-lumican. This provides counsel for plausible exploitation of lumican and graphene oxide as targeted/nano drug delivery system to reinstate acute wounds, chronic wounds, corneal wounds, hypertrophic scars and keloids in near future. Communicated by Ramaswamy H. Sarma.

Learning peptide recognition rules for a low-specificity protein

Many proteins interact with short linear regions of target proteins. For some proteins, however, it is difficult to identify a well-defined sequence motif that defines its target peptides. To overcome this difficulty, we used supervised machine learning to train a model that treats each peptide as a collection of easily-calculated biochemical features rather than as an amino acid sequence. As a test case, we dissected the peptide-recognition rules for human S100A5 (hA5), a low-specificity calcium binding protein. We trained a Random Forest model against a recently released, high-throughput phage display dataset collected for hA5. The model identifies hydrophobicity and shape complementarity, rather than polar contacts, as the primary determinants of peptide binding specificity in hA5. We tested this hypothesis by solving a crystal structure of hA5 and through computational docking studies of diverse peptides onto hA5. These structural studies revealed that peptides exhibit multiple binding modes at the hA5 peptide interface-all of which have few polar contacts with hA5. Finally, we used our trained model to predict new, plausible binding targets in the human proteome. This revealed a fragment of the protein α-1-syntrophin that binds to hA5. Our work helps better understand the biochemistry and biology of hA5, as well as demonstrating how high-throughput experiments coupled with machine learning of biochemical features can reveal the determinants of binding specificity in low-specificity proteins.

Engineering G-quadruplex aptamer to modulate its binding specificity

The use of aptamers in bioanalytical and biomedical applications exploits their ability to recognize cell surface protein receptors. Targeted therapeutics and theranostics come to mind in this regard. However, protein receptors occur on both cancer and normal cells; as such, aptamers are now taxed with identifying high vs. low levels of protein expression. Inspired by the flexible template mechanism and elegant control of natural nucleic acid-based structures, we report an allosteric regulation strategy for constructing a structure-switching aptamer for enhanced target cell recognition by engineering aptamers with DNA intercalated motifs (i-motifs) responsive to the microenvironment, such as pH. Structure-switching sensitivity can be readily tuned by manipulating i-motif sequences. However, structure-switching sensitivity is difficult to estimate, making it equally difficult to effectively screen modified aptamers with the desired sensitivity. To address this problem, we selected a fluorescent probe capable of detecting G-quadruplex in complicated biological media.

Identification of hemicatenane-specific binding proteins by fractionation of HeLa nuclei extracts

DNA hemicatenanes (HCs) are four-way junctions in which one strand of a double-stranded helix is catenated with one strand of another double-stranded DNA. Frequently mentioned as DNA replication, recombination and repair intermediates, they have been proposed to participate in the spatial organization of chromosomes and in the regulation of gene expression. To explore potential roles of HCs in genome metabolism, we sought to purify proteins capable of binding specifically HCs by fractionating nuclear extracts from HeLa cells. This approach identified three RNA-binding proteins: the Tudor-staphylococcal nuclease domain 1 (SND1) protein and two proteins from the Drosophila behavior human splicing family, the paraspeckle protein component 1 and the splicing factor proline- and glutamine-rich protein. Since these proteins were partially pure after fractionation, truncated forms of these proteins were expressed in Escherichia coli and purified to near homogeneity. The specificity of their interaction with HCs was re-examined in vitro. The two truncated purified SND1 proteins exhibited specificity for HCs, opening the interesting possibility of a link between the basic transcription machinery and HC structures via SND1.

PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction

Interactions between proteins and DNAs play essential roles in many biological processes. DNA binding proteins can be classified into two categories. Double-stranded DNA-binding proteins (DSBs) bind to double-stranded DNA and are involved in a series of cell functions such as gene expression and regulation. Single-stranded DNA-binding proteins (SSBs) are necessary for DNA replication, recombination, and repair and are responsible for binding to the single-stranded DNA. Therefore, the effective classification of DNA-binding proteins is helpful for functional annotations of proteins. In this work, we propose PredPSD, a computational method based on sequence information that accurately predicts SSBs and DSBs. It introduces three novel feature extraction algorithms. In particular, we use the autocross-covariance (ACC) transformation to transform feature matrices into fixed-length vectors. Then, we put the optimal feature subset obtained by the minimal-redundancy-maximal-relevance criterion (mRMR) feature selection algorithm into the gradient tree boosting (GTB). In 10-fold cross-validation based on a benchmark dataset, PredPSD achieves promising performances with an AUC score of 0.956 and an accuracy of 0.912, which are better than those of existing methods. Moreover, our method has significantly improved the prediction accuracy in independent testing. The experimental results show that PredPSD can significantly recognize the binding specificity and differentiate DSBs and SSBs.

Observation of unexpected molecular binding activity for Mu phage tail fibre chaperones

In the history of viral research, one of the important biological features of bacteriophage Mu is the ability to expand its host range. For extending the host range, the Mu phage encodes two alternate tail fibre genes. Classical amber mutation experiments and genome sequence analysis of Mu phage suggested that gene products (gp) of geneS (gpS = gp49) and gene S' (gpS' = gp52) are tail fibres and that gene products of geneU (gpU = gp50) and geneU' (gpU' = gp51) work for tail fibre assembly or tail fibre chaperones. Depending on the gene orientation, a pair of genes 49-50 or 52-51 is expressed for producing different tail fibres that enable Mu phage to recognize different host cell surface. Since several fibrous proteins including some phage tail fibres employ their specific chaperone to facilitate folding and prevent aggregation, we expected that gp50 or gp51 would be a specific chaperone for gp49 and gp52, respectively. However, heterologous overexpression results for gp49 or gp52 (tail fibre subunit) together with gp51 and gp50, respectively, were also effective in producing soluble Mu tail fibres. Moreover, we successfully purified non-native gp49-gp51 and gp52-gp50 complexes. These facts showed that gp50 and gp51 were fungible and functional for both gp49 and gp52 each other.

Balance between DNA-binding affinity and specificity enables selective recognition of longer target sequences in vivo

Although genome-editing enzymes such as TALEN and CRISPR/Cas9 are being widely used, they have an essential limitation in that their relatively high-molecular weight makes them difficult to be delivered to cells. To develop a novel genome-editing enzyme with a smaller molecular weight, we focused on the engrailed homeodomain (EHD). We designed and constructed proteins composed of two EHDs connected by a linker to increase sequence specificity. In bacterial one-hybrid assays and electrophoresis mobility shift assay analyses, the created proteins exhibited good affinity for DNA sequences consisting of two tandemly aligned EHD target sequences. However, they also bound to individual EHD targets. To avoid binding to single target sites, we introduced amino acid mutations to reduce the protein-DNA affinity of each EHD monomer and successfully created a small protein with high specificity for tandem EHD target sequences.

Trinucleotide Base Pair Stacking Free Energy for Understanding TF-DNA Recognition and the Functions of SNPs

Single nucleotide polymorphisms (SNPs) affect base pair stacking, which is the primary factor for maintaining the stability of DNA. However, the mechanism of how SNPs lead to phenotype variations is still unclear. In this work, we connected SNPs and base pair stacking by a 3-mer base pair stacking free energy matrix. The SNPs with large base pair stacking free energy differences led to phenotype variations. A molecular dynamics (MD) simulation was then applied. Our results showed that base pair stacking played an important role in the transcription factor (TF)-DNA interaction. Changes in DNA structure mainly originate from TF-DNA interactions, and with the increased base pair stacking free energy, the structure of DNA approaches its free type, although its binding affinity was increased by the SNP. In addition, quantitative models using base pair stacking features revealed that base pair stacking can be used to predict TF binding specificity. As such, our work combined knowledge from bioinformatics and structural biology and provided a new understanding of the relationship between SNPs and phenotype variations. The 3-mer base pair stacking free energy matrix is useful in high-throughput screening of SNPs and predicting TF-DNA binding affinity.

Allosteric Modulation of Binding Specificity by Alternative Packing of Protein Cores

Hydrophobic cores are often viewed as tightly packed and rigid, but they do show some plasticity and could thus be attractive targets for protein design. Here we explored the role of different functional pressures on the core packing and ligand recognition of the SH3 domain from human Fyn tyrosine kinase. We randomized the hydrophobic core and used phage display to select variants that bound to each of three distinct ligands. The three evolved groups showed remarkable differences in core composition, illustrating the effect of different selective pressures on the core. Changes in the core did not significantly alter protein stability, but were linked closely to changes in binding affinity and specificity. Structural analysis and molecular dynamics simulations revealed the structural basis for altered specificity. The evolved domains had significantly reduced core volumes, which in turn induced increased backbone flexibility. These motions were propagated from the core to the binding surface and induced significant conformational changes. These results show that alternative core packing and consequent allosteric modulation of binding interfaces could be used to engineer proteins with novel functions.

Differential Substrate Recognition by Maltose Binding Proteins Influenced by Structure and Dynamics

The genome of the hyperthermophile Thermotoga maritima contains three isoforms of maltose binding protein (MBP) that are high-affinity receptors for di-, tri-, and tetrasaccharides. Two of these proteins (tmMBP1 and tmMBP2) share significant sequence identity, approximately 90%, while the third (tmMBP3) shares less than 40% identity. MBP from Escherichia coli (ecMBP) shares 35% sequence identity with the tmMBPs. This subset of MBP isoforms offers an interesting opportunity to investigate the mechanisms underlying the evolution of substrate specificity and affinity profiles in a genome where redundant MBP genes are present. In this study, the X-ray crystal structures of tmMBP1, tmMBP2, and tmMBP3 are reported in the absence and presence of oligosaccharides. tmMBP1 and tmMBP2 have binding pockets that are larger than that of tmMBP3, enabling them to bind to larger substrates, while tmMBP1 and tmMBP2 also undergo substrate-induced hinge bending motions (∼52°) that are larger than that of tmMBP3 (∼35°). Small-angle X-ray scattering was used to compare protein behavior in solution, and computer simulations provided insights into dynamics of these proteins. Comparing quantitative protein-substrate interactions and dynamical properties of tmMBPs with those of the promiscuous ecMBP and disaccharide selective Thermococcus litoralis MBP provides insights into the features that enable selective binding. Collectively, the results provide insights into how the structure and dynamics of tmMBP homologues enable them to differentiate between a myriad of chemical entities while maintaining their common fold.

Combinatorial engineering of N-TIMP2 variants that selectively inhibit MMP9 and MMP14 function in the cell

Developing selective inhibitors for proteolytic enzymes that share high sequence homology and structural similarity is important for achieving high target affinity and functional specificity. Here, we used a combination of yeast surface display and dual-color selective library screening to obtain selective inhibitors for each of the matrix metalloproteinases (MMPs) MMP14 and MMP9 by modifying the non-specific N-terminal domain of the tissue inhibitor of metalloproteinase-2 (N-TIMP2). We generated inhibitor variants with 30- to 1175-fold improved specificity to each of the proteases, respectively, relative to wild type N-TIMP2. These biochemical results accurately predicted the selectivity and specificity obtained in cell-based assays. In U87MG cells, the activation of MMP2 by MMP14 was inhibited by MMP14-selective blockers but not MMP9-specific inhibitors. Target specificity was also demonstrated in MCF-7 cells stably expressing either MMP14 or MMP9, with only the MMP14-specific inhibitors preventing the mobility of MMP14-expressing cells. Similarly, the mobility of MMP9-expressing cells was inhibited by the MMP9-specific inhibitors, yet was not altered by the MMP14-specific inhibitors. The strategy developed in this study for improving the specificity of an otherwise broad-spectrum inhibitor will likely enhance our understanding of the basis for target specificity of inhibitors to proteolytic enzymes, in general, and to MMPs, in particular. We, moreover, envision that this study could serve as a platform for the development of next-generation, target-specific therapeutic agents. Finally, our methodology can be extended to other classes of proteolytic enzymes and other important target proteins.

Converting a broad matrix metalloproteinase family inhibitor into a specific inhibitor of MMP-9 and MMP-14

MMP-14 and MMP-9 are two well-established cancer targets for which no specific clinically relevant inhibitor is available. Using a powerful combination of computational design and yeast surface display technology, we engineered such an inhibitor starting from a nonspecific MMP inhibitor, N-TIMP2. The engineered purified N-TIMP2 variants showed enhanced specificity toward MMP-14 and MMP-9 relative to a panel of off-target MMPs. MMP-specific N-TIMP2 sequence signatures were obtained that could be understood from the structural perspective of MMP/N-TIMP2 interactions. Our MMP-9 inhibitor exhibited 1000-fold preference for MMP-9 vs. MMP-14, which is likely to translate into significant differences under physiological conditions. Our results provide new insights regarding evolution of promiscuous proteins and optimization strategies for design of inhibitors with single-target specificities.

Many Routes to an Antibody Heavy-Chain CDR3: Necessary, Yet Insufficient, for Specific Binding

Because of its great potential for diversity, the immunoglobulin heavy-chain complementarity-determining region 3 (HCDR3) is taken as an antibody molecule’s most important component in conferring binding activity and specificity. For this reason, HCDR3s have been used as unique identifiers to investigate adaptive immune responses in vivo and to characterize in vitro selection outputs where display systems were employed. Here, we show that many different HCDR3s can be identified within a target-specific antibody population after in vitro selection. For each identified HCDR3, a number of different antibodies bearing differences elsewhere can be found. In such selected populations, all antibodies with the same HCDR3 recognize the target, albeit at different affinities. In contrast, within unselected populations, the majority of antibodies with the same HCDR3 sequence do not bind the target. In one HCDR3 examined in depth, all target-specific antibodies were derived from the same VDJ rearrangement, while non-binding antibodies with the same HCDR3 were derived from many different V and D gene rearrangements. Careful examination of previously published in vivo datasets reveals that HCDR3s shared between, and within, different individuals can also originate from rearrangements of different V and D genes, with up to 26 different rearrangements yielding the same identical HCDR3 sequence. On the basis of these observations, we conclude that the same HCDR3 can be generated by many different rearrangements, but that specific target binding is an outcome of unique rearrangements and VL pairing: the HCDR3 is necessary, albeit insufficient, for specific antibody binding.

Influential Mutations in the SMAD4 Trimer Complex Can Be Detected from Disruptions of Electrostatic Complementarity

This article examines three techniques for rapidly assessing the electrostatic contribution of individual amino acids to the stability of protein-protein complexes. Whereas the energetic minimization of modeled oligomers may yield more accurate complexes, we examined the possibility that simple modeling may be sufficient to identify amino acids that add to or detract from electrostatic complementarity. The three methods evaluated were (a) the elimination of entire side chains (e.g., glycine scanning), (b) the elimination of the electrostatic contribution from the atoms of a side chain, called nullification, and (c) side chain structure prediction using SCWRL4. These techniques generate models in seconds, enabling large-scale mutational scanning. We evaluated these techniques on the SMAD2/SMAD4 heterotrimer, whose formation plays a crucial role in antitumor pathways. Many studies have documented the clinical and structural effect of specific mutations on trimer formation. Our results describe how glycine scanning yields more specific predictions, although nullification may be more sensitive, and how side chain structure prediction enables the identification of uncharged-to-charge mutations.

Transcription factor family-specific DNA shape readout revealed by quantitative specificity models

Transcription factors (TFs) achieve DNA‐binding specificity through contacts with functional groups of bases (base readout) and readout of structural properties of the double helix (shape readout). Currently, it remains unclear whether DNA shape readout is utilized by only a few selected TF families, or whether this mechanism is used extensively by most TF families. We resequenced data from previously published HT‐SELEX experiments, the most extensive mammalian TF–DNA binding data available to date. Using these data, we demonstrated the contributions of DNA shape readout across diverse TF families and its importance in core motif‐flanking regions. Statistical machine‐learning models combined with feature‐selection techniques helped to reveal the nucleotide position‐dependent DNA shape readout in TF‐binding sites and the TF family‐specific position dependence. Based on these results, we proposed novel DNA shape logos to visualize the DNA shape preferences of TFs. Overall, this work suggests a way of obtaining mechanistic insights into TF–DNA binding without relying on experimentally solved all‐atom structures.

Epistatic mutations in PUMA BH3 drive an alternate binding mode to potently and selectively inhibit anti-apoptotic Bfl-1

Overexpression of anti-apoptotic Bcl-2 family proteins contributes to cancer progression and confers resistance to chemotherapy. Small molecules that target Bcl-2 are used in the clinic to treat leukemia, but tight and selective inhibitors are not available for Bcl-2 paralog Bfl-1. Guided by computational analysis, we designed variants of the native BH3 motif PUMA that are > 150-fold selective for Bfl-1 binding. The designed peptides potently trigger disruption of the mitochondrial outer membrane in cells dependent on Bfl-1, but not in cells dependent on other anti-apoptotic homologs. High-resolution crystal structures show that designed peptide FS2 binds Bfl-1 in a shifted geometry, relative to PUMA and other binding partners, due to a set of epistatic mutations. FS2 modified with an electrophile reacts with a cysteine near the peptide-binding groove to augment specificity. Designed Bfl-1 binders provide reagents for cellular profiling and leads for developing enhanced and cell-permeable peptide or small-molecule inhibitors.

A novel human truncated IL12rβ1-Fc fusion protein ameliorates experimental autoimmune encephalomyelitis via specific binding of p40 to inhibit Th1 and Th17 cell differentiation

Interleukin (IL)-12 and IL-23 respectively driving polarization of T helper (Th) 1 and Th17 cells has been strongly implicated in the pathogenesis of both multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). In this study, we first constructed, expressed and purified a novel human truncated IL12rβ1-Fc fusion protein (tIL12rβ1/Fc) binding multiple forms of the p40 subunit of human IL-12 and IL-23. tIL12rβ1/Fc was found to effectively ameliorate MOG_35–55-induced EAE through reducing the production of Th1- and Th17-polarized pro-inflammatory cytokines and suppressing inflammation and demyelination in the focused parts. Moreover, tIL12rβ1/Fc suppressed Th1 (IFN-γ⁺ alone) and IFN-γ⁺ IL-17⁺ as well as the population of classic Th17 (IL-17⁺ alone) cells in vivo. Furthermore, tIL12rβ1/Fc ameliorated EAE at the peak of disease via the inhibition of STAT pathway, thereby causing a prominent reduction of RORγt (Th17) and T-bet (Th1) expression. Notably, tIL12rβ1/Fc could increase the relative number of CD4⁺ Foxp3⁺ regulatory T cells. These findings indicates that tIL12rβ1/Fc is a novel fusion protein for specific binding multiple forms of p40 subunit to exert potent anti-inflammatory effects and provides a valuable approach for the treatment of MS and other autoimmune diseases.

Native Mass Spectrometry in Fragment-Based Drug Discovery

The advent of native mass spectrometry (MS) in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein-ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD). Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

Evolution of domain-peptide interactions to coadapt specificity and affinity to functional diversity

Evolution of complexity in eukaryotic proteomes has arisen, in part, through emergence of modular independently folded domains mediating protein interactions via binding to short linear peptides in proteins. Over 30 years, structural properties and sequence preferences of these peptides have been extensively characterized. Less successful, however, were efforts to establish relationships between physicochemical properties and functions of domain-peptide interactions. To our knowledge, we have devised the first strategy to exhaustively explore the binding specificity of protein domain-peptide interactions. We applied the strategy to SH3 domains to determine the properties of their binding peptides starting from various experimental data. The strategy identified the majority (∼70%) of experimentally determined SH3 binding sites. We discovered mutual relationships among binding specificity, binding affinity, and structural properties and evolution of linear peptides. Remarkably, we found that these properties are also related to functional diversity, defined by depth of proteins within hierarchies of gene ontologies. Our results revealed that linear peptides evolved to coadapt specificity and affinity to functional diversity of domain-peptide interactions. Thus, domain-peptide interactions follow human-constructed gene ontologies, which suggest that our understanding of biological process hierarchies reflect the way chemical and thermodynamic properties of linear peptides and their interaction networks, in general, have evolved.

Molecular determinants of the binding specificity of BH3 ligands to BclXL apoptotic repressor

B-cell lymphoma extra-large protein (BclXL) serves as an apoptotic repressor by virtue of its ability to recognize and bind to BH3 domains found within a diverse array of proapoptotic regulators. Herein, we investigate the molecular basis of the specificity of the binding of proapoptotic BH3 ligands to BclXL. Our data reveal that while the BH3 ligands harboring the LXXX[A/S]D and [R/Q]XLXXXGD motif bind to BclXL with high affinity in the submicromolar range, those with the LXXXGD motif afford weak interactions. This suggests that the presence of a glycine at the fourth position (G+4)--relative to the N-terminal leucine (L0) within the LXXXGD motif--mitigates binding, unless the LXXXGD motif also contains arginine/glutamine at the -2 position. Of particular note is the observation that the residues at the +4 and -2 positions within the LXXX[A/S]D and [R/Q]XLXXXGD motifs appear to be energetically coupled-replacement of either [A/S]+4 or [R/Q]-2 with other residues has little bearing on the binding affinity of BH3 ligands harboring one of these motifs. Collectively, our study lends new molecular insights into understanding the binding specificity of BH3 ligands to BclXL with important consequences on the design of novel anticancer drugs.

Novel dipeptidyl hydroxamic acids that inhibit human and bacterial dipeptidyl peptidase III

Human dipeptidyl peptidase III (hDPP III), a zinc-metallopeptidase of the family M49, is an activator of the Keap1-Nrf2 cytoprotective pathway involved in defense against oxidative stress. Pathophysiological roles of DPP III have not been elucidated yet, partly due to the lack of specific inhibitors. We showed that substrate analog H-Tyr-Phe-NHOH is a strong competitive inhibitor of hDPP III, while H-Tyr-Gly-NHOH expresses much weaker inhibition. To investigate the effects of amino acid substitutions in inhibitor P1 position, we synthesized three new dipeptidyl hydroxamates and examined their influence on the activity of hDPP III and DPP III from the human gut symbiont Bacteroides thetaiotaomicron. The extent of inhibition of hDPP III, but not of bacterial enzyme, was dependent on the amino acid in P1. H-Phe-Phe-NHOH is recognized as one of the strongest inhibitors of hDPP III (K_i = 0.028 μM), and H-Phe-Leu-NHOH discriminated between human and bacterial ortholog of the M49 family.

Conformational Sampling Reveals Amino Acids with a Steric Influence on Specificity

Flexible representations of protein structures can enable structure comparison algorithms to find remotely homologous proteins, even when they have been crystallized in different conformations. By compensating for large spatial variations, these representations can enable these algorithms to better detect remote similarities in the space of protein structures. Subtle variations in protein structures can also have a substantial impact structure comparison. For example, the motion of a single side chain into a binding cavity can make the cavity appear totally dissimilar to identical binding sites, even though, in reality, the presence of the side chain does not affect binding. To address the impact of subtle conformational variations, this article describes FAVA (Flexible Aggregate Volumetric Analysis), an algorithm that enables comparisons of ligand binding sites while compensating for subtle, localized flexibility. FAVA integrates hundreds of conformational samples, sourced from any molecular simulation software that provides all-atom detail, to characterize the geometry of ligand binding sites as they frequently appear. This representation enables rare conformations, as defined by the user, to be excluded from the structural comparison. In our results, on three families of serine proteases and three families of enolases, we show that despite substantial binding site variations, FAVA is able to correctly classify families with different binding preferences. We also demonstrate that FAVA can examine the motion of individual amino acids to identify those that influence ligand binding specificity. Together, these capabilities demonstrate that comparison errors associated with small conformational variations, which can substantially alter the geometry of ligand binding sites and other local features, can be mitigated by an analysis of many conformational samples.

Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect

Solvation effect is an important factor for protein-ligand binding in aqueous water. Previous scoring function of protein-ligand interactions rarely incorporates the solvation model into the quantification of protein-ligand interactions, mainly due to the immense computational cost, especially in the structure-based virtual screening, and nontransferable application of independently optimized atomic solvation parameters. In order to overcome these barriers, we effectively combine knowledge-based atom-pair potentials and the atomic solvation energy of charge-independent implicit solvent model in the optimization of binding affinity and specificity. The resulting scoring functions with optimized atomic solvation parameters is named as specificity and affinity with solvation effect (SPA-SE). The performance of SPA-SE is evaluated and compared to 20 other scoring functions, as well as SPA. The comparative results show that SPA-SE outperforms all other scoring functions in binding affinity prediction and "native" pose identification. Our optimization validates that solvation effect is an important regulator to the stability and specificity of protein-ligand binding. The development strategy of SPA-SE sets an example for other scoring function to account for the solvation effect in biomolecular recognitions.

Insights into the binding specificity of wild type and mutated wheat germ agglutinin towards Neu5Acα(2-3)Gal: a study by in silico mutations and molecular dynamics simulations

Wheat germ agglutinin (WGA) is a plant lectin, which specifically recognizes the sugars NeuNAc and GlcNAc. Mutated WGA with enhanced binding specificity can be used as biomarkers for cancer. In silico mutations are performed at the active site of WGA to enhance the binding specificity towards sialylglycans, and molecular dynamics simulations of 20 ns are carried out for wild type and mutated WGAs (WGA1, WGA2, and WGA3) in complex with sialylgalactose to examine the change in binding specificity. MD simulations reveal the change in binding specificity of wild type and mutated WGAs towards sialylgalactose and bound conformational flexibility of sialylgalactose. The mutated polar amino acid residues Asn114 (S114N), Lys118 (G118K), and Arg118 (G118R) make direct and water mediated hydrogen bonds and hydrophobic interactions with sialylgalactose. An analysis of possible hydrogen bonds, hydrophobic interactions, total pair wise interaction energy between active site residues and sialylgalactose and MM-PBSA free energy calculation reveals the plausible binding modes and the role of water in stabilizing different binding modes. An interesting observation is that the binding specificity of mutated WGAs (cyborg lectin) towards sialylgalactose is found to be higher in double point mutation (WGA3). One of the substituted residues Arg118 plays a crucial role in sugar binding. Based on the interactions and energy calculations, it is concluded that the order of binding specificity of WGAs towards sialylgalactose is WGA3 > WGA1 > WGA2 > WGA. On comparing with the wild type, double point mutated WGA (WGA3) exhibits increased specificity towards sialylgalactose, and thus, it can be effectively used in targeted drug delivery and as biological cell marker in cancer therapeutics.

Investigation of arc repressor DNA-binding specificity by comparative molecular dynamics simulations

Transcription factors regulate gene expression through binding to specific DNA sequences. How transcription factors achieve high binding specificity is still not well understood. In this paper, we investigated the role of protein flexibility in protein-DNA-binding specificity by comparative molecular dynamics (MD) simulations. Protein flexibility has been considered as a key factor in molecular recognition, which is intrinsically a dynamic process involving fine structural fitting between binding components. In this study, we performed comparative MD simulations on wild-type and F10V mutant P22 Arc repressor in both free and complex conformations. The F10V mutant has lower DNA-binding specificity though both the bound and unbound main-chain structures between the wild-type and F10V mutant Arc are highly similar. We found that the DNA-binding motif of wild-type Arc is structurally more flexible than the F10V mutant in the unbound state, especially for the six DNA base-contacting residues in each dimer. We demonstrated that the flexible side chains of wild-type Arc lead to a higher DNA-binding specificity through forming more hydrogen bonds with DNA bases upon binding. Our simulations also showed a possible conformational selection mechanism for Arc-DNA binding. These results indicate the important roles of protein flexibility and dynamic properties in protein-DNA-binding specificity.

A minimal model of protein-protein binding affinities

A minimal model of protein-protein binding affinity that takes into account only two structural features of the complex, the size of its interface, and the amplitude of the conformation change between the free and bound subunits, is tested on the 144 complexes of a structure-affinity benchmark. It yields Kd values that are within two orders of magnitude of the experiment for 67% of the complexes, within three orders for 88%, and fails on 12%, which display either large conformation changes, or a very high or a low affinity. The minimal model lacks the specificity and accuracy needed to make useful affinity predictions, but it should help in assessing the added value of parameters used by more elaborate models, and set a baseline for evaluating their performances.

Specific RNA-binding antibodies with a four-amino-acid code

Numerous large non-coding RNAs are rapidly being discovered, and many of them have been shown to play vital roles in gene expression, gene regulation, and human diseases. Given their often structured nature, specific recognition with an antibody fragment becomes feasible and may help define the structure and function of these non-coding RNAs. As demonstrated for protein antigens, specific antibodies may aid in RNA crystal structure elucidation or the development of diagnostic tools and therapeutic drugs targeting disease-causing RNAs. Recent success and limitation of RNA antibody development has made it imperative to generate an effective antibody library specifically targeting RNA molecules. Adopting the reduced chemical diversity design and further restricting the interface diversity to tyrosines, serines, glycines, and arginines only, we have constructed a RNA-targeting Fab library. Phage display selection and downstream characterization showed that this library yielded high-affinity Fabs for all three RNA targets tested. Using a quantitative specificity assay, we found that these Fabs are highly specific, possibly due to the alternate codon design we used to avoid consecutive arginines in the Fab interface. In addition, the effectiveness of the minimal Fab library may challenge our view of the protein-RNA binding interface and provide a unique solution for future design of RNA-binding proteins.

Probing the role of interfacial waters in protein-DNA recognition using a hybrid implicit/explicit solvation model

When proteins bind to their DNA target sites, ordered water molecules are often present at the protein-DNA interface bridging protein and DNA through hydrogen bonds. What is the role of these ordered interfacial waters? Are they important determinants of the specificity of DNA sequence recognition, or do they act in binding in a primarily nonspecific manner, by improving packing of the interface, shielding unfavorable electrostatic interactions, and solvating unsatisfied polar groups that are inaccessible to bulk solvent? When modeling details of structure and binding preferences, can fully implicit solvent models be fruitfully applied to protein-DNA interfaces, or must the individualistic properties of these interfacial waters be accounted for? To address these questions, we have developed a hybrid implicit/explicit solvation model that specifically accounts for the locations and orientations of small numbers of DNA-bound water molecules, while treating the majority of the solvent implicitly. Comparing the performance of this model with that of its fully implicit counterpart, we find that explicit treatment of interfacial waters results in a modest but significant improvement in protein side-chain placement and DNA sequence recovery. Base-by-base comparison of the performance of the two models highlights DNA sequence positions whose recognition may be dependent on interfacial water. Our study offers large-scale statistical evidence for the role of ordered water for protein-DNA recognition, together with detailed examination of several well-characterized systems. In addition, our approach provides a template for modeling explicit water molecules at interfaces that should be extensible to other systems.

Nanoparticle design optimization for enhanced targeting: Monte Carlo simulations

Using computer simulations, we systematically studied the influence of different design parameters of a spherical nanoparticle tethered with monovalent ligands on its efficiency of targeting planar cell surfaces containing mobile receptors. We investigate how the nanoparticle affinity can be affected by changing the binding energy, the percent of functionalization by ligands, tether length, grafting density, and nanoparticle core size. In general, using a longer tether length or increasing the number of tethered chains without increasing the number of ligands increases the conformational penalty for tether stretching/compression near the cell surface and leads to a decrease in targeting efficiency. At the same time, using longer tethers or a larger core size allows ligands to interact with receptors over a larger cell surface area, which can enhance the nanoparticle affinity toward the cell surface. We also discuss the selectivity of nanoparticle targeting of cells with a high receptor density. Based on the obtained results, we provide recommendations for improving the nanoparticle binding affinity and selectivity, which can guide future nanoparticle development for diagnostic and therapeutic purposes.

NetMHCpan, a method for MHC class I binding prediction beyond humans

Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide-MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method's ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.

HIV-1 protease inhibitors from inverse design in the substrate envelope exhibit subnanomolar binding to drug-resistant variants

The acquisition of drug-resistant mutations by infectious pathogens remains a pressing health concern, and the development of strategies to combat this threat is a priority. Here we have applied a general strategy, inverse design using the substrate envelope, to develop inhibitors of HIV-1 protease. Structure-based computation was used to design inhibitors predicted to stay within a consensus substrate volume in the binding site. Two rounds of design, synthesis, experimental testing, and structural analysis were carried out, resulting in a total of 51 compounds. Improvements in design methodology led to a roughly 1000-fold affinity enhancement to a wild-type protease for the best binders, from a Ki of 30-50 nM in round one to below 100 pM in round two. Crystal structures of a subset of complexes revealed a binding mode similar to each design that respected the substrate envelope in nearly all cases. All four best binders from round one exhibited broad specificity against a clinically relevant panel of drug-resistant HIV-1 protease variants, losing no more than 6-13-fold affinity relative to wild type. Testing a subset of second-round compounds against the panel of resistant variants revealed three classes of inhibitors: robust binders (maximum affinity loss of 14-16-fold), moderate binders (35-80-fold), and susceptible binders (greater than 100-fold). Although for especially high-affinity inhibitors additional factors may also be important, overall, these results suggest that designing inhibitors using the substrate envelope may be a useful strategy in the development of therapeutics with low susceptibility to resistance.

Modeling backbone flexibility to achieve sequence diversity: the design of novel alpha-helical ligands for Bcl-xL

Computational protein design can be used to select sequences that are compatible with a fixed-backbone template. This strategy has been used in numerous instances to engineer novel proteins. However, the fixed-backbone assumption severely restricts the sequence space that is accessible via design. For challenging problems, such as the design of functional proteins, this may not be acceptable. Here, we present a method for introducing backbone flexibility into protein design calculations and apply it to the design of diverse helical BH3 ligands that bind to the anti-apoptotic protein Bcl-xL, a member of the Bcl-2 protein family. We demonstrate how normal mode analysis can be used to sample different BH3 backbones, and show that this leads to a larger and more diverse set of low-energy solutions than can be achieved using a native high-resolution Bcl-xL complex crystal structure as a template. We tested several of the designed solutions experimentally and found that this approach worked well when normal mode calculations were used to deform a native BH3 helix structure, but less well when they were used to deform an idealized helix. A subsequent round of design and testing identified a likely source of the problem as inadequate sampling of the helix pitch. In all, we tested 17 designed BH3 peptide sequences, including several point mutants. Of these, eight bound well to Bcl-xL and four others showed weak but detectable binding. The successful designs showed a diversity of sequences that would have been difficult or impossible to achieve using only a fixed backbone. Thus, introducing backbone flexibility via normal mode analysis effectively broadened the set of sequences identified by computational design, and provided insight into positions important for binding Bcl-xL.

Prediction of interacting proteins from homology-modeled complex structures using sequence and structure scores

Protein-protein interactions support most biological processes, and it is important to find specifically interacting partner proteins among homologous proteins in order to elucidate cellular functions such as signal transduction systems. Various high-throughput experimental methods for identifying these interactions have been invented, and used to generate a huge amount of data. Because these experiments have been applied to only a few organisms, and their accuracy is believed to be limited, it would be valuable to develop computational methods for predicting protein-protein interactions from their amino acid sequences or tertiary structural information. In this study, we describe a prediction method of interacting proteins based on homology-modeled complex structures. We employed the statistical residue-residue contact energy used in a previous study, and two types of new scores, simple electrostatic energy and sequence similarity between target sequences and template structures. The validity of each protein-protein complex model was measured using their single and combined scores. We applied our method to all the protein heterodimers of Saccharomyces cerevisiae. To evaluate the prediction performance of our method, we prepared two types of protein-protein interaction dataset: a complete dataset and high confidence dataset. The complete dataset (10,325 protein dimer models) contains all the yeast protein heterodimers whose complex structures can be modeled. Among them, pairs registered in the DIP database are defined as interacting pairs, and those not registered are defined as non-interacting protein pairs. The high confidence dataset (3,219 protein dimer models) is a more reliable subset of the complete dataset extracted using the criteria of the common subcellular localization. Both datasets show that sequence similarity has a much higher discrimination power than the other structure-based scores, but that the inclusion of contact energy results in significant improvement over predictions using sequence similarity alone. These results suggest that the sequence similarity is indispensable for the prediction, whereas structure scores can play supporting roles.

A quantitative description of the binding states and in vitro function of antitermination protein N of bacteriophage lambda

The N protein of bacteriophage lambda activates transcription of genes that lie downstream of termination sequences by suppressing transcription termination. N binds to specific (boxB) and non-specific sites on the transcript RNA and contacts RNA polymerase via cis-RNA looping, resulting in "antitermination" of transcription. To find the effect of N-boxB binding on antitermination, we quantitatively relate binding measurements made in isolation to in vitro antitermination activity. We measure binding of N to boxB RNA, non-specific single-stranded RNA, and non-specific double-stranded DNA fluorimetrically, and use an equilibrium model to describe quantitatively the binding of N to nucleic acids of Escherichia coli transcription elongation complexes. We then test the model by comparison with in vitro N antitermination activity measured in reactions containing these same elongation complexes. We find that binding of N protein to the nucleic acid components of transcription elongation complexes can quantitatively predict antitermination activity, suggesting that antitermination in vitro is determined by a nucleic acid binding equilibrium with one molecule of N protein per RNA transcript being sufficient for antitermination. Elongation complexes contain numerous overlapping non-specific RNA and DNA-binding sites for N; the large number of sites compensates for the low N binding affinity, so multiple N proteins are expected to bind to elongation complexes. The occupancy/activity of these proteins is described by a binomial distribution of proteins on transcripts containing multiple non-specific sites. The contribution of specific (boxB) binding to activity also depends on this distribution. Specificity is not measured accurately by measurements made in the presence and in the absence of boxB. We find that antitermination is inhibited by non-productive binding of N to non-specific sites on template DNA, and that NusA protein covers RNA sites on the transcript, limiting N access and activity. The activity and specificity of regulatory proteins that loop from high-affinity binding sites are likely modulated by multiple non-specific binding events; in vivo activity may also be regulated by the modulation of non-specific binding.