PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity

The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔG_bind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔG_bind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔG_bind data points on purified proteins to generate ΔΔG_bind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔG_bind for this interaction could be quantified with high accuracy over the range of 12 kcal mol⁻¹ displayed by various BPTI single mutants.

Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.

βC1, pathogenicity determinant encoded by Cotton leaf curl Multan betasatellite, interacts with calmodulin-like protein 11 (Gh-CML11) in Gossypium hirsutum

Begomoviruses interfere with host plant machinery to evade host defense mechanism by interacting with plant proteins. In the old world, this group of viruses are usually associated with betasatellite that induces severe disease symptoms by encoding a protein, βC1, which is a pathogenicity determinant. Here, we show that βC1 encoded by Cotton leaf curl Multan betasatellite (CLCuMB) requires Gossypium hirsutum calmodulin-like protein 11 (Gh-CML11) to infect cotton. First, we used the in silico approach to predict the interaction of CLCuMB-βC1 with Gh-CML11. A number of sequence- and structure-based in-silico interaction prediction techniques suggested a strong putative binding of CLCuMB-βC1 with Gh-CML11 in a Ca+2-dependent manner. In-silico interaction prediction was then confirmed by three different experimental approaches: The Gh-CML11 interaction was confirmed using CLCuMB-βC1 in a yeast two hybrid system and pull down assay. These results were further validated using bimolecular fluorescence complementation system showing the interaction in cytoplasmic veins of Nicotiana benthamiana. Bioinformatics and molecular studies suggested that CLCuMB-βC1 induces the overexpression of Gh-CML11 protein and ultimately provides calcium as a nutrient source for virus movement and transmission. This is the first comprehensive study on the interaction between CLCuMB-βC1 and Gh-CML11 proteins which provided insights into our understating of the role of βC1 in cotton leaf curl disease.

Docking-based approach for identification of mutations that disrupt binding between Bcl-2 and Bax proteins: Inducing apoptosis in cancer cells

BACKGROUND

Inducing apoptosis in cancer cells is an important step for the successful treatment of cancer patients. Bcl-2 is an antiapoptotic protein which determines apoptosis by interacting with proapoptotic members of the Bcl-2 family. Exome sequencing has identified Bcl-2 and Bax missense mutations in more than 40 cancer types. However, a little information is available about the functional impact of each Bcl-2 and Bax mutation on the pathogenesis of cancer.

METHODS

The mutational data from cancer tissues and cell lines were retrieved from the cBioPortal web resource. The 13 mutated Bcl-2 and wild-type Bax complexes with experimentally verified binding were identified from previous studies wherein, binding for all complexes was reportedly disrupted except one. Several protein-protein docking methods such as ClusPro, HDOCK, PatchDock, FireDock, InterEVDock2 and several mutation prediction methods such as PolyPhen-2, SIFT, and OncoKB have been used to predict the effect of mutation to disrupt the binding between Bcl-2 and Bax. The result obtained was compared with the known experimental data.

RESULTS

The protein-protein docking method, ClusPro, employed in the present study confirmed that the binding affinity of 11 out of 13 complexes decreases. Similarly, binding affinity computed for all the 10 wild-type Bcl-2 and mutated Bax complexes agreed with experimentally verified results.

CONCLUSION

Several methods like PolyPhen-2, SIFT, and OncoKB have been developed to predict cancer-associated or deleterious mutations, but no method is available to predict apoptosis-inducing mutations. Thus, in this study, we have examined the mutations in Bcl-2 and Bax proteins that disrupt their binding, which is crucial for inducing apoptosis to eradicate cancer. This study suggests that protein-protein docking methods can play a significant role in the identification of hotspot mutations in Bcl-2 or Bax that can disrupt their binding with wild-type partner to induce apoptosis in cancer cells.

Nuclear-Cytoplasmic Coevolution Analysis of RuBisCO in Synthesized Cucumis Allopolyploid

Allopolyploids are often faced with the challenge of maintaining well-coordination between nuclear and cytoplasmic genes inherited from different species. The synthetic allotetraploid Cucumis × hytivus is a useful model to explore cytonuclear coevolution. In this study, the sequences and expression of cytonuclear enzyme complex RuBisCO as well as its content and activity in C. × hytivus were compared to its parents to explore plastid–nuclear coevolution. The plastome-coded rbcL gene sequence was confirmed to be stable maternal inheritance, and parental copy of nuclear rbcS genes were both preserved in C. × hytivus. Thus, the maternal plastid may interact with the biparentally inherited rbcS alleles. The expression of the rbcS gene of C-homoeologs (paternal) was significantly higher than that of H-homoeologs (maternal) in C. × hytivus (HHCC). Protein interaction prediction analysis showed that the rbcL protein has stronger binding affinity to the paternal copy of rbcS protein than that of maternal copy in C. × hytivus, which might explain the transcriptional bias of the rbcS homoeologs. Moreover, both the activity and content of RuBisCO in C. × hytivus showed mid-parent heterosis. In summary, our results indicate a paternal transcriptional bias of the rbcS genes in C. × hytivus, and we found new nuclear–cytoplasmic combination may be one of the reasons for allopolyploids heterosis.

Functional characterization of the translation initiation factor eIF4E of Echinococcus granulosus

The eukaryotic initiation factor 4E (eIF4E) specifically recognizes the 5' mRNA cap, a rate-limiting step in the translation initiation process. Although the 7-methylguanosine cap (MMGcap) is the most common 5' cap structure in eukaryotes, the trans-splicing process that occurs in several organism groups, including nematodes and flatworms, leads to the addition of a trimethylguanosine cap (TMGcap) to some RNA transcripts. In some helminths, eIF4E can have a dual capacity to bind both MMGcap and TMGcap. In the present work, we evaluated the distribution of eIF4E protein sequences in platyhelminths and we showed that only one gene coding for eIF4E is present in most parasitic flatworms. Based on this result, we cloned the Echinococcus granulosus cDNA sequence encoding eIF4E in Escherichia coli, expressed the recombinant eIF4E as a fusion protein to GST, and tested its ability to capture mRNAs through the 5' cap using pull-down assay and qPCR. Our results indicate that the recombinant eIF4E was able to bind preferentially 5'-capped mRNAs compared with rRNAs from total RNA preparations of E. granulosus. By qPCR, we observed an enrichment in MMG-capped mRNA compared with TMG-capped mRNAs among Eg-eIF4E-GST pull-down RNAs. Eg-eIF4E structural model using the Schistosoma mansoni eIF4E as template showed to be well preserved with only a few differences between chemically similar amino acid residues at the binding sites. These data showed that E. granulosus eIF4E can be used as a potential tool to study full-length 5'-capped mRNA, besides being a potential drug target against parasitic flatworms.

Multifaceted protein-protein interaction prediction based on Siamese residual RCNN

MOTIVATION

Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.

RESULTS

We present an end-to-end framework, PIPR (Protein-Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.

AVAILABILITY AND IMPLEMENTATION

The implementation is available at https://github.com/muhaochen/seq_ppi.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico Prediction and Validations of Domains Involved in Gossypium hirsutum SnRK1 Protein Interaction With Cotton Leaf Curl Multan Betasatellite Encoded βC1

Cotton leaf curl disease (CLCuD) caused by viruses of genus Begomovirus is a major constraint to cotton (Gossypium hirsutum) production in many cotton-growing regions of the world. Symptoms of the disease are caused by Cotton leaf curl Multan betasatellite (CLCuMB) that encodes a pathogenicity determinant protein, βC1. Here, we report the identification of interacting regions in βC1 protein by using computational approaches including sequence recognition, and binding site and interface prediction methods. We show the domain-level interactions based on the structural analysis of G. hirsutum SnRK1 protein and its domains with CLCuMB-βC1. To verify and validate the in silico predictions, three different experimental approaches, yeast two hybrid, bimolecular fluorescence complementation and pull down assay were used. Our results showed that ubiquitin-associated domain (UBA) and autoinhibitory sequence (AIS) domains of G. hirsutum-encoded SnRK1 are involved in CLCuMB-βC1 interaction. This is the first comprehensive investigation that combined in silico interaction prediction followed by experimental validation of interaction between CLCuMB-βC1 and a host protein. We demonstrated that data from computational biology could provide binding site information between CLCuD-associated viruses/satellites and new hosts that lack known binding site information for protein-protein interaction studies. Implications of these findings are discussed.

A Comprehensive Review on Current Advances in Peptide Drug Development and Design

Protein-protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide-protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide-protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide-protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.

Cysteine-Rich Antifungal Proteins from Filamentous Fungi are Promising Bioactive Natural Compounds in Anti-Candida Therapy

The emerging number of life-threatening invasive fungal infections caused by drug-resistant Candida strains urges the need for the development and application of fundamentally new and safe antifungal strategies in the clinical treatment. Recent studies demonstrated that the extracellular cysteine-rich and cationic antifungal proteins (crAFPs) originating from filamentous fungi, and de novo designed synthetic peptide derivatives of these crAFPs provide a feasible basis for this approach. This mini-review focuses on the global challenges of the anti-Canidia therapy and on the crAFPs as potential drug candidates to overcome existing problems. The advantages and limitations in the use of crAFPs and peptide derivatives compared to those of conventional antifungal drugs will also be critically discussed.

Binding of a Naja naja venom acidic phospholipase A2 cognate complex to membrane-bound vimentin of rat L6 cells: Implications in cobra venom-induced cytotoxicity

An acidic phospholipase A₂ enzyme (NnPLA₂-I) interacts with three finger toxins (cytotoxin and neurotoxin) from Naja naja venom to form cognate complexes to enhance its cytotoxicity towards rat L6 myogenic cells. The cytotoxicity was further enhanced in presence of trace quantity of venom nerve growth factor. The purified rat myoblast cell membrane protein showing interaction with NnPLA₂-I was identified as vimentin by LC-MS/MS analysis. The ELISA, immunoblot and spectrofluorometric analyses showed greater binding of NnPLA₂-I cognate complex to vimentin as compared to the binding of individual NnPLA₂-I. The immunofluorescence and confocal microscopy studies evidenced the internalization of NnPLA₂-I to partially differentiated myoblasts post binding with vimentin in a time-dependent manner. Pre-incubation of polyvalent antivenom with NnPLA₂-I cognate complex demonstrated better neutralization of cytotoxicity towards L6 cells as compared to exogenous addition of polyvalent antivenom 60-240 min post treatment of L6 cells with cognate complex suggesting clinical advantage of early antivenom treatment to prevent cobra venom-induced cytotoxicity. The in silico analysis showed that 19-22 residues, inclusive of Asp48 residue, of NnPLA₂-I preferentially binds with the rod domain (99-189 and 261-335 regions) of vimentin with a predicted free binding energy (ΔG) and dissociation constant (K_D) values of -12.86 kcal/mol and 3.67 × 10^-10 M, respectively; however, NnPLA₂-I cognate complex showed greater binding with the same regions of vimentin indicating the pathophysiological significance of cognate complex in cobra venom-induced cytotoxicity.

In Vivo Applicability of Neosartorya fischeri Antifungal Protein 2 (NFAP2) in Treatment of Vulvovaginal Candidiasis

As a consequence of emerging numbers of vulvovaginitis cases caused by azole-resistant and biofilm-forming Candida species, fast and efficient treatment of this infection has become challenging. The problem is further exacerbated by the severe side effects of azoles as long-term-use medications in the recurrent form. There is therefore an increasing demand for novel and safely applicable effective antifungal therapeutic strategies. The small, cysteine-rich, and cationic antifungal proteins from filamentous ascomycetes are potential candidates, as they inhibit the growth of several Candida spp. in vitro; however, no information is available about their in vivo antifungal potency against yeasts. In the present study, we investigated the possible therapeutic application of one of their representatives in the treatment of vulvovaginal candidiasis, Neosartorya fischeri antifungal protein 2 (NFAP2). NFAP2 inhibited the growth of a fluconazole (FLC)-resistant Candida albicans strain isolated from a vulvovaginal infection, and it was effective against both planktonic cells and biofilm in vitro We observed that the fungal cell-killing activity of NFAP2 is connected to its pore-forming ability in the cell membrane. NFAP2 did not exert cytotoxic effects on primary human keratinocytes and dermal fibroblasts at the MIC in vitro. In vivo murine vulvovaginitis model experiments showed that NFAP2 significantly decreases the number of FLC-resistant C. albicans cells, and combined application with FLC enhances the efficacy. These results suggest that NFAP2 provides a feasible base for the development of a fundamental new, safely applicable mono- or polytherapeutic topical agent for the treatment of superficial candidiasis.

Learning protein binding affinity using privileged information

BACKGROUND

Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data.

RESULTS

In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well.

CONCLUSIONS

The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well.

TROP-2 exhibits tumor suppressive functions in cervical cancer by dual inhibition of IGF-1R and ALK signaling

OBJECTIVE

Inactivation of tumor suppressor genes promotes initiation and progression of cervical cancer. This study aims to investigate the tumor suppressive effects of TROP-2 in cervical cancer cells and to explain the underlying mechanisms.

METHODS

The tumor suppressive functions of TROP-2 in cervical cancer cells were examined by in vitro and in vivo tumorigenic functional assays. Downstream factors of TROP-2 were screened using Human Phospho-Receptor Tyrosine Kinase Array. Small molecule inhibitors were applied to HeLa cells to test the TROP-2 effects on the oncogenicity of IGF-1R and ALK. Protein interactions between TROP-2 and the ligands of IGF-1R and ALK were detected via immunoprecipitation assay and protein-protein affinity prediction.

RESULTS

In vitro and in vivo functional assays showed that overexpression of TROP-2 significantly inhibited the oncogenicity of cervical cancer cells; while knockdown of TROP-2 exhibited opposite effects. Human Phospho-Receptor Tyrosine Kinase Array showed that the activity of IGF-1R and ALK was stimulated by TROP-2 knockdown. Small molecule inhibitors AG1024 targeting IGF-1R and Crizotinib targeting ALK were treated to HeLa cells with and without TROP-2 overexpression, and results from cell viability and migration assays indicated that the oncogenicity of vector-transfected cells was repressed to a greater extent by the inhibition of either IGF-1R or ALK than that of the TROP-2-overexpressed cells. Immunoprecipitation assay and protein-protein affinity prediction suggested protein interactions between TROP-2 and the ligands of IGF-1R and ALK.

CONCLUSIONS

Collectively, our results support that TROP-2 exhibits tumor suppressor functions in cervical cancer through inhibiting the activity of IGF-1R and ALK.

ProBAPred: Inferring protein–protein binding affinity by incorporating protein sequence and structural features

Protein-protein binding interaction is the most prevalent biological activity that mediates a great variety of biological processes. The increasing availability of experimental data of protein–protein interaction allows a systematic construction of protein–protein interaction networks, significantly contributing to a better understanding of protein functions and their roles in cellular pathways and human diseases. Compared to well-established classification for protein–protein interactions (PPIs), limited work has been conducted for estimating protein–protein binding free energy, which can provide informative real-value regression models for characterizing the protein–protein binding affinity. In this study, we propose a novel ensemble computational framework, termed ProBAPred (Protein–protein Binding Affinity Predictor), for quantitative estimation of protein–protein binding affinity. A large number of sequence and structural features, including physical–chemical properties, binding energy and conformation annotations, were collected and calculated from currently available protein binding complex datasets and the literature. Feature selection based on the WEKA package was performed to identify and characterize the most informative and contributing feature subsets. Experiments on the independent test showed that our ensemble method achieved the lowest Mean Absolute Error (MAE; 1.657[Formula: see text]kcal/mol) and the second highest correlation coefficient ([Formula: see text]), compared with the existing methods. The datasets and source codes of ProBAPred, and the supplementary materials in this study can be downloaded at http://lightning.med.monash.edu/probapred/ for academic use. We anticipate that the developed ProBAPred regression models can facilitate computational characterization and experimental studies of protein–protein binding affinity.

Molecular weight impact on the mechanical forces between hyaluronan and its receptor

Hyaluronan (HA) possesses manifold mechanical and signaling properties in the body. Most of these activities are largely regulated by its molecular weight, which often triggers opposing functions. However the molecular basis for such function distinction between HA size categories remains unclear. Using a combination of biophysical techniques, we measured the physical forces between HA ligand and its specific receptor CD44 in both normal and lateral directions, at different HA molecular weights and bound states. It was found that the impact of HA multivalency is more than just the sum of separate monovalent bindings. The HA-CD44 specific interaction enhances with HA molecular weight and the maximum binding occurs at ∼1000 kD, possibly due to the balance between multivalent HA zipping effect and conformational entropy. High friction patches, probably from CD44 protein clustering, was observed in friction force microscopy (FFM) upon HA shearing, which is also dependent on HA molecular weight. These results could help to understand the biophysical mechanism of HA in regulating CD44-induced physiological activities and thus facilitate the new design of HA-based material in fine tuning the receptor responses.

Partner-Specific Prediction of Protein-Dimer Stability from Unbound Structure of Monomer

Protein complexes play deterministic roles in live entities in sensing, compiling, controlling, and responding to external and internal stimuli. Thermodynamic stability is an important property of protein complexes; having knowledge about complex stability helps us to understand the basics of protein assembly-related diseases and the mechanism of protein assembly clearly. Enormous protein-protein interactions, detected by high-throughput methods, necessitate finding fast methods for predicting the stability of protein assemblies in a quantitative and qualitative manner. The existing methods of predicting complex stability need knowledge about the three-dimensional (3D) structure of the intended protein complex. Here, we introduce a new method for predicting dissociation free energy of subunits by analyzing the structural and topological properties of a protein binding patch on a single subunit of the desired protein complex. The method needs the 3D structure of just one subunit and the information about the position of the intended binding site on the surface of that subunit to predict dimer stability in a classwise manner. The patterns of structural and topological properties of a protein binding patch are decoded by recurrence quantification analysis. Nonparametric discrimination is then utilized to predict the stability class of the intended dimer with accuracy greater than 85%.

Redefining the Protein-Protein Interface: Coarse Graining and Combinatorics for an Improved Understanding of Amino Acid Contributions to the Protein-Protein Binding Affinity

The ability to intervene in biological pathways has for decades been limited by the lack of a quantitative description of protein-protein interactions (PPIs). Herein we generate and compare millions of simple PPI models for insight into the mechanisms of specific recognition and binding. We use a coarse-grained approach whereby amino acids are counted in the interface, and these counts are used as binding affinity predictors. We perform lasso regression, a modern regression technique aimed at interpretability, with every possible amino acid combination (over 10⁶ unique feature sets) to select only those amino acid predictors that provide more information than noise. This approach circumvents arbitrary binning and assumptions about the binding environment that obscure other binding affinity models. Aggregated analysis of these models trained at various interfacial cutoff distances informs the roles of specific amino acids in different binding contexts. We find that a simple amino acid count model outperforms detailed intermolecular contact and binned residue type models. We identify the prevalence of serine, glycine, and tryptophan in the interface as particularly important for predicting binding affinity across a range of distance cutoffs. Although current sample size limitations prevent a robust consensus model for binding affinity prediction, our approach underscores the relevance of a residue-based description of the protein-protein interface to increase our understanding of specific interactions.

Modeling non-clinical and clinical drug tests in Gaucher disease

There is need for modeling biological systems to accelerate drug pipelines for treating metabolic diseases. The eliglustat treatment for Gaucher disease is approved by the FDA with a companion genomic test. The Transcriptome-To-Metabolome™ biosimulation technology was used to model, in silico, a standard non-clinical eliglustat test with an in vitro canine kidney cell system over-expressing a human gene; and a clinical test using human fibroblasts from control and Gaucher disease subjects. Protein homology modeling and docking studies were included to gather affinity parameters for the kinetic metabolic model. Pharmacodynamics and metabolomics analyses of the results replicated published findings and demonstrated that processing and transport of lysosomal proteins alone cannot explain the metabolic disorder. This technology shows promise for application to other diseases.

Molecular and structural characterization of novel cystatins from the taiga tick Ixodes persulcatus

Cystatins are cysteine peptidase inhibitors that in ticks mediate processes such as blood feeding and digestion. The ixodid tick Ixodes persulcatus is endemic to the Eurasia, where it is the principal vector of Lyme borreliosis. To date, no I. persulcatus cystatin has been characterized. In the present work, we describe three novel cystatins from I. persulcatus, named JpIpcys2a, JpIpcys2b and JpIpcys2c. In addition, the potential of tick cystatins as cross-protective antigens was evaluated by vaccination of hamsters using BrBmcys2c, a cystatin from Rhipicephalus microplus, against I. persulcatus infestation. Sequence analysis showed that motifs that are characteristic of cystatins type 2 are fully conserved in JpIpcys2b, while mutations are present in both JpIpcys2a and JpIpcys2c. Protein-protein docking simulations further revealed that JpIpcys2a, JpIpcys2b and JpIpcys2c showed conserved binding sites to human cathepsins L, all of them covering the active site cleft. Cystatin transcripts were detected in different I. persulcatus tissues and instars, showing their ubiquitous expression during I. persulcatus development. Serological analysis showed that although hamsters immunized with BrBmcys2c developed a humoral immune response, this response was not adequate to protect against a heterologous challenge with I. persulcatus adult ticks. The lack of cross-protection provided by BrBmcys2c immunization is perhaps linked to the fact that cystatins cluster into multigene protein families that are expressed differentially and exhibit functional redundancy. How to target such small proteins that are secreted in low quantities remains a challenge in the development of suitable anti-tick vaccine antigens.

Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes

Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.

Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes

Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.

An unexpected way forward: towards a more accurate and rigorous protein-protein binding affinity scoring function by eliminating terms from an already simple scoring function

A fundamental and unsolved problem in biophysical chemistry is the development of a computationally simple, physically intuitive, and generally applicable method for accurately predicting and physically explaining protein-protein binding affinities from protein-protein interaction (PPI) complex coordinates. Here, we propose that the simplification of a previously described six-term PPI scoring function to a four term function results in a simple expression of all physically and statistically meaningful terms that can be used to accurately predict and explain binding affinities for a well-defined subset of PPIs that are characterized by (1) crystallographic coordinates, (2) rigid-body association, (3) normal interface size, and hydrophobicity and hydrophilicity, and (4) high quality experimental binding affinity measurements. We further propose that the four-term scoring function could be regarded as a core expression for future development into a more general PPI scoring function. Our work has clear implications for PPI modeling and structure-based drug design.

Protein-protein interactions: scoring schemes and binding affinity

Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.

PDBparam: Online Resource for Computing Structural Parameters of Proteins

Understanding the structure-function relationship in proteins is a longstanding goal in molecular and computational biology. The development of structure-based parameters has helped to relate the structure with the function of a protein. Although several structural features have been reported in the literature, no single server can calculate a wide-ranging set of structure-based features from protein three-dimensional structures. In this work, we have developed a web-based tool, PDBparam, for computing more than 50 structure-based features for any given protein structure. These features are classified into four major categories: (i) interresidue interactions, which include short-, medium-, and long-range interactions, contact order, long-range order, total contact distance, contact number, and multiple contact index, (ii) secondary structure propensities such as α-helical propensity, β-sheet propensity, and propensity of amino acids to exist at various positions of α-helix and amino acid compositions in high B-value regions, (iii) physicochemical properties containing ionic interactions, hydrogen bond interactions, hydrophobic interactions, disulfide interactions, aromatic interactions, surrounding hydrophobicity, and buriedness, and (iv) identification of binding site residues in protein-protein, protein-nucleic acid, and protein-ligand complexes. The server can be freely accessed at http://www.iitm.ac.in/bioinfo/pdbparam/. We suggest the use of PDBparam as an effective tool for analyzing protein structures.

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

Background

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only.

Results

This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn.

Conclusions

The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Linking structural features of protein complexes and biological function

Protein-protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure-function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.

Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).