Protein-protein Interaction Profiles Research Articles

Intracellular Photocatalytic Proximity Labeling (iPPL) for Dynamic Analysis of Chromatin-Binding Proteins Targeting Histone H3.

We demonstrated a novel approach for protein-protein interaction (PPI) profiling of histone H3 using intracellular photocatalytic-proximity labeling (iPPL). This approach identified that the combination of acriflavine as a photocatalyst and 1-methyl-4-arylurazol (MAUra) as a protein labeling agent was the most efficient strategy to proceed the protein proximity labeling reaction. Furthermore, the identification of the labeled amino acids in histone H3 interacting proteins, histone lysine N-methyltransferase EZH2, showed that the amino acid in EZH2 within a few nanometers from histone H3 is labeled by iPPL. This restricted labeling radius allows for more-focused PPI profiling, compared to conventional proximity labeling methods.

Altered Protein Palmitoylation as Disease Mechanism in Neurodegenerative Disorders.

Palmitoylation, a lipid-based posttranslational protein modification, plays a crucial role in regulating various aspects of neuronal function through altering protein membrane-targeting, stabilities, and protein-protein interaction profiles. Disruption of palmitoylation has recently garnered attention as disease mechanism in neurodegeneration. Many proteins implicated in neurodegenerative diseases and associated neuronal dysfunction, including but not limited to amyloid precursor protein, β-secretase (BACE1), postsynaptic density protein 95, Fyn, synaptotagmin-11, mutant huntingtin, and mutant superoxide dismutase 1, undergo palmitoylation, and recent evidence suggests that altered palmitoylation contributes to the pathological characteristics of these proteins and associated disruption of cellular processes. In addition, dysfunction of enzymes that catalyze palmitoylation and depalmitoylation has been connected to the development of neurological disorders. This review highlights some of the latest advances in our understanding of palmitoylation regulation in neurodegenerative diseases and explores potential therapeutic implications.

Cell surface protein-protein interaction profiling for biological network analysis and novel target discovery.

The secretome is composed of cell surface membrane proteins and extracellular secreted proteins that are synthesized via secretory machinery, accounting for approximately one-third of human protein-encoding genes and playing central roles in cellular communication with the external environment. Secretome protein-protein interactions (SPPIs) mediate cell proliferation, apoptosis, and differentiation, as well as stimulus- or cell-specific responses that regulate a diverse range of biological processes. Aberrant SPPIs are associated with diseases including cancer, immune disorders, and illness caused by infectious pathogens. Identifying the receptor/ligand for a secretome protein or pathogen can be a challenging task, and many SPPIs remain obscure, with a large number of orphan receptors and ligands, as well as viruses with unknown host receptors, populating the SPPI network. In addition, proteins with known receptors/ligands may also interact with alternative uncharacterized partners and exert context-dependent effects. In the past few decades, multiple varied approaches have been developed to identify SPPIs, and these methods have broad applications in both basic and translational research. Here, we review and discuss the technologies for SPPI profiling and the application of these technologies in identifying novel targets for immunotherapy and anti-infectious agents.

Formulation of next-generation polyvalent vaccine candidates against three important poxviruses by targeting DNA-dependent RNA polymerase using an integrated immunoinformatics and molecular modeling approach

BackgroundPoxviruses comprise a group of large double-stranded DNA viruses and are known to cause diseases in humans, livestock animals, and other animal species. The Mpox virus (MPXV; formerly Monkeypox), variola virus (VARV), and volepox virus (VPXV) are among the prevalent poxviruses of the Orthopoxviridae genera. The ongoing Mpox infectious disease pandemic caused by the Mpox virus has had a major impact on public health across the globe. To date, only limited repurposed antivirals and vaccines are available for the effective treatment of Mpox and other poxviruses that cause contagious diseases. MethodsThe present study was conducted with the primary goal of formulating multi-epitope vaccines against three evolutionary closed poxviruses i.e., MPXV, VARV, and VPXV using an integrated immunoinformatics and molecular modeling approach. DNA-dependent RNA polymerase (DdRp), a potential vaccine target of poxviruses, has been used to determine immunodominant B and T-cell epitopes followed by interactions analysis with Toll-like receptor 2 at the atomic level. ResultsThree multi-epitope vaccine constructs, namely DdRp_MPXV (V1), DdRp_VARV (V2), and DdRp_VPXV (V3) were designed. These vaccine constructs were found to be antigenic, non-allergenic, non-toxic, and soluble with desired physicochemical properties. Protein-protein docking and interaction profiling analysis depicts a strong binding pattern between the targeted immune receptor TLR2 and the structural models of the designed vaccine constructs, and manifested a number of biochemical bonds (hydrogen bonds, salt bridges, and non-bonded contacts). State-of-the-art all-atoms molecular dynamics simulations revealed highly stable interactions of vaccine constructs with TLR2 at the atomic level throughout the simulations on 300 nanoseconds. Additionally, the outcome of the immune simulation analysis suggested that designed vaccines have the potential to induce protective immunity against targeted poxviruses. ConclusionsTaken together, formulated next-generation polyvalent vaccines were found to have good efficacy against closely related poxviruses (MPXV, VARV, and VPXV) as demonstrated by our extensive immunoinformatics and molecular modeling evaluations; however, further experimental investigations are still needed.

In silico investigation on the mutational analysis of BRCA1-BARD1 RING domains and its effect on nucleosome recognition and ubiquitination

The BRCA1-BARD1 complex is a crucial tumor suppressor E3 ubiquitin ligase involved in DNA double-stranded break repair. The BRCA1-BARD1 RING domains interact with UBE2D3 through the BRCA1 interface and this complex flexibly tether to the nucleosome core particle (NCP), where BRCA1 and BARD1 interacts with histone H2A and H2B of NCP. Mutations in the BRCA1-BARD1 RING domains have been linked to familial breast and ovarian cancer. Seven mutations were analyzed to understand their effect on the binding interface of protein partners and changes in conformational dynamics. Molecular dynamics simulations revealed that mutant complexes were less conformationally flexible than the wildtype complex. Protein-protein interaction profiling showed the importance of specific molecular interactions, hotspot and hub residues, and some of these were lost in the mutant complexes. Two mutations (BRCA1L51W-K65R and BARD1C53W) hindered significant interaction between protein partners and may prevent signaling for ubiquitination of histones in NCP and other cellular targets. The structural compactness and reduced significant interaction in mutant complexes may be the possible reason of preventing ubiquitination and hinder DNA repair, resulting cancer.

Self-association features of NS1 proteins from different flaviviruses

Flaviviruses comprise a large group of arboviral species that are distributed in several countries of the tropics, neotropics, and some temperate zones. Since they can produce neurological pathologies or vascular damage, there has been intense research seeking better diagnosis and treatments for their infections in the last decades. The flavivirus NS1 protein is a relevant clinical target because it is involved in viral replication, immune evasion, and virulence. Being a key factor in endothelial and tissue-specific modulation, NS1 has been largely studied to understand the molecular mechanisms exploited by the virus to reprogram host cells. A central part of the viral maturation processes is the NS1 oligomerization because many stages rely on these protein-protein assemblies. In the present study, the self-associations of NS1 proteins from Zika, Dengue, and West Nile viruses are examined through constant-pH coarse-grained biophysical simulations. Free energies of interactions were estimated for different oligomeric states and pH conditions. Our results show that these proteins can form both dimers and tetramers under conditions near physiological pH even without the presence of lipids. Moreover, pH plays an important role mainly controlling the regimes where van der Waals interactions govern their association. Finally, despite the similarity at the sequence level, we found that each flavivirus has a well-characteristic protein-protein interaction profile. These specific features can provide new hints for the development of binders both for better diagnostic tools and the formulation of new therapeutic drugs.

Identification and Expression Analysis of Calcium-Dependent Protein Kinases Gene Family in Potato Under Drought Stress.

Calcium-dependent protein kinases (CDPKs) are a class of serine/threonine protein kinases encoded by several gene families that play key roles in stress response and plant growth and development. In this study, the BLAST method was used to search for protein sequences of the potato Calcium-dependent protein kinase gene family. The chromosome location, phylogeny, gene structures, gene duplication, cis-acting elements, protein-protein interaction, and expression profiles were analyzed. Twenty-five CDPK genes in the potato genome were identified based on RNA-seq data and were clustered into four groups (I-IV) based on their structural features and phylogenetic analysis. The result showed the composition of the promoter region of the StCDPKs gene, including light-responsive elements such as Box4, hormone-responsive elements such as ABRE, and stress-responsive elements such as MBS. Four pairs of segmental duplications were found in StCDPKs genes and the Ka/Ks ratios were below 1, indicating a purifying selection of the genes. The protein-protein interaction network revealed defense-related proteins such as; respiratory burst oxidase homologs (RBOHs) interacting with potato CDPKs. Transcript abundance was measured via RT-PCR between the two cultivars and their relative expression of CDPK genes was analyzed after 15, 20, and 25 days of drought. There were varied expression patterns of StCDPK3/13/21 and 23, between the two potato cultivars under mannitol induced-drought conditions. Correlation analysis showed that StCDPK21/22 and StCDPK3 may be the major differentially expressed genes involved in the regulation of malondialdehyde (MDA) and proline content in response to drought stress, opening a new research direction for genetic improvement of drought resistance in potato.

A proteomic analysis of Pseudevernia furfuracea after exposure to Cr+6 by MALDI-TOF mass spectrometry.

The problem of heavy metal pollution in nature has increased rapidly in recent years. Hexavalent chromium (Cr+6) is one of the most toxic heavy metals that cause environmental pollution. Although many studies in the literature that illuminate the stress response mechanisms of biological organisms such as bacteria, algae, and plants against heavy metals, there is limited information about revealing the protein level changes of lichen species in response to heavy metal stress. Here, we used a MALDI-TOF-based proteomic assay to determine protein level changes in Pseudevernia furfuracea after exposure to Cr+6 heavy metal stress at 6, 18 and 24h. It was determined that expression levels of 26, 149 and 66 proteins changed in P. furfuracea. 6, 18 and 24 h after Cr+6 application compared to the control sample, respectively. We identified 9 common proteins expressed at three different time levels (6, 18, 24h) and evaluated their protein-protein interaction profiles with the STRING tool. According to the results of the study, it was determined that the expression level of six proteins was up-regulated (OP4, KIP3, BNI5, VSP64, HSP 60, BCK1) and three proteins were down-regulated (MNS1, ABZ2, ATG4) from the expression level of nine proteins in total with Cr+6 exposure. It was determined that nine proteins were also found to be effective in biological processes such as stress signaling, transcription regulation and cellular detoxification metabolisms. To confirm the protein expression level, we analyzed the HSP60 protein by western blot assay. It has been shown that exposure to Cr+6 exposure in P. furfuracea caused an increase in HSP60 protein level compared to the control sample (non-exposed Cr+6). In this study, new knowledge are presented for the use of P. furfuracea as a biosorption agent in the removal of industrial wastes in biotechnological applications.

Genome-wide identification and comprehensive analyses of NAC transcription factor gene family and expression patterns during somatic embryogenesis in Dimocarpus longan Lour

The NAM, ATAF1/2, and CUC2 form a huge plant-specific gene family of NAC TFs that are involved in the growth, development, and regulation of biotic and abiotic stress responses. Although the draft genome of longan (Dimocarpus longan Lour.) has been published, however the comprehensive data regarding the functions, evolution, and expression patterns of the NAC family are still unavailable. In this study, a comprehensive analysis of the NAC transcription factor family in longan was performed, and a total of 114 NAC genes were found. We investigated the NAC gene family exploring the phylogeny, domain conservation, intron/exon, motifs, cis-regulatory elements, protein-protein interaction, and expression profiles of RNA-seq samples in different tissues and early somatic embryogenesis of longan. Phylogenetic analysis showed that the genes with similar gene structure and motif distribution were clustered in the same group. Cis-element identification indicates the possible role of NAC genes in biological and physiological processes. Protein-protein interaction identified the DlNACs homologous with Arabidopsis proteins. We further investigated the expression pattern of DlNAC genes in different tissues (pulp, stem, large fruit, young fruit, and flower) during somatic embryogenesis at embryogenic callus (EC), incomplete compact pro-embryogenic cultures (ICpEC), and globular embryos (GE) stages. The qRT-PCR results showed that the DlNAC genes were expressed higher at EC and GE stage compared with ICpEC stage. In conclusion, our results provide insight into the evolution, diversity, and characterization of NAC genes in the longan and provide a base for understanding their biological roles and molecular mechanisms in plants.

Abstract P4-10-26: Protein-protein interaction of HER2 predicts the prognosis of hormone receptor-positive HER2-negative breast cancer

Abstract Introduction: Patients with Hormone receptor (HR)-positive / HER2-negtive breast cancer have not received targeted therapy for Her2 based on criteria of the protein expression or DNA copy number amplification. However, the clinical meaning of the level of Her2 signaling activity has not been fully elucidated yet. We recently developed a novel technique of single-molecule protein-protein interaction (PPI) profiling to investigate the importance of HER2 signaling activity in this major subgroup of breast cancer. Methods: This study is a study with the biospecimen and registered medical record from the prospective collected cohort of Samsung Medical Center, and all of these biospecimens were provided by Samsung Medical Center BioBank. First, We selected 138 of 8463 patients who were diagnosed with invasive breast cancer by curative surgery between January 2008 and December 2013. Specimens were selected based on modified Allred Immunohistochemistry (IHC) scores; 6 for hormone receptors (ER/PR+) and 1 for Her2. Then, we applied the single-molecule co-immunoprecipitation, a novel technology determining protein-protein interactions (PPIs), to each selected biospecimen. PPI and expression level for HER2 protein of each tumor extract were analyzed with HER2-PPI-Dx kit. All the measurement were performed using a single-molecule fluorescence imaging equipment. Based on HER2 PPI and expression level analysis, we developed an algorithm based on the 72 patients’ samples which can predict the prognosis of these patients. This algorithm was validated with the samples from the other 66 patients. Results: In total, 138 samples from each patients (72 in training set and 66 in validation set) were analyzed. The median follow-up time was 68.0 (range: 7-123) months. Single-molecule measurement for HER2 level showed that clinical specimens showed significant differences in HER2-PPI level even though they had the same IHC score (65-fold from 117 patients with ‘HER2 IHC score: 0’ and 33-fold from 21 patients with ‘HER2-IHC score: 1’, respectively). For the training set, by combining HER2 PPI and HER2 protein expression data, we could select 12 patients (17%) as a high level HER2-PPI group. This high level HER2-PPI group showed the significantly poorer disease free survival (DFS) rate compared to that of the low level group (42% for high-level vs. 83% for low-level, p=0.001). For the validation set with the same cutoff, 17 patients (26%) with identified as high level HER2-PPI group. We still found a significantly poorer DFS rate in this group compared to that of the low level group (82% vs. 88%, p=0.033). For total 138 patients set, we found a significantly poorer DFS rate in this group compared to that of the low level group (p=0.014). Conclusion: Our results showed that HER2 PPI may have an important clinical meaning to identify a high risk of recurrence in the patients with strong HR-positive and HER2-negative breast cancers defined by conventional method. For those patients with high level HER2-PPI, HER2-targeted therapies may be beneficial despite of their HER2-negativity. This will be proved for the future. Citation Format: Hee Jun Choi, Hong Won Lee, Sangmin Kim, Tae-Young Yoon, Hyunwoo Kim, Seul Lee, Seok Jin Nam, Seok Won Kim, Sooyoun Cho, Yeon Hee Park, Jeong Eon Lee. Protein-protein interaction of HER2 predicts the prognosis of hormone receptor-positive HER2-negative breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-26.

Investigating host-bacterial interactions among enteric pathogens

BackgroundIn 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant “priority pathogens” that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them.ResultsUsing our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species of Escherichia, Shigella, Salmonella and Vibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed.ConslusionsThe identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.

The nuclear interactome of DYRK1A reveals a functional role in DNA damage repair

The chromosome 21 encoded protein kinase DYRK1A is essential for normal human development. Mutations in DYRK1A underlie a spectrum of human developmental disorders, and increased dosage in trisomy 21 is implicated in Down syndrome related pathologies. DYRK1A regulates a diverse array of cellular processes through physical interactions with substrates and binding partners in various subcellular compartments. Despite recent large-scale protein-protein interaction profiling efforts, DYRK1A interactions specific to different subcellular compartments remain largely unknown, impeding progress toward understanding emerging roles for this kinase. Here, we used immunoaffinity purification and quantitative mass spectrometry to identify nuclear interaction partners of endogenous DYRK1A. This interactome was enriched in DNA damage repair factors, transcriptional elongation factors and E3 ubiquitin ligases. We validated an interaction with RNF169, a factor that promotes homology directed repair upon DNA damage, and found that DYRK1A expression and kinase activity are required for maintenance of 53BP1 expression and subsequent recruitment to DNA damage loci. Further, DYRK1A knock out conferred resistance to ionizing radiation in colony formation assays, suggesting that DYRK1A expression decreases cell survival efficiency in response to DNA damage and points to a tumor suppressive role for this kinase.

Integration of protein interaction and gene co-expression information for identification of melanoma candidate genes

Cutaneous melanoma is an aggressive form of skin cancer that causes death worldwide. Although much has been learned about the molecular basis of melanoma genesis and progression, there is also increasing appreciation for the continuing discovery of melanoma genes to improve the genetic understanding of this malignancy. In the present study, melanoma candidate genes were identified by analysis of the common network from cancer type-specific RNA-Seq co-expression data and protein-protein interaction profiles. Then, an integrated network containing the known melanoma-related genes represented as seed genes and the putative genes represented as linker genes was generated using the subnetwork extraction algorithm. According to the network topology property of the putative genes, we selected seven key genes (CREB1, XPO1, SP3, TNFRSF1B, CD40LG, UBR1, and ZNF484) as candidate genes of melanoma. Subsequent analysis showed that six of these genes are melanoma-associated genes and one (ZNF484) is a cancer-associated gene on the basis of the existing literature. A signature comprising these seven key genes was developed and an overall survival analysis of 461 cutaneous melanoma cases was carried out. This seven-gene signature can accurately determine the risk profile for cutaneous melanoma tumors (log-rank P=3.27E-05) and be validated on an independent clinical cohort (log-rank P=0.028). The presented seven genes might serve as candidates for studying the molecular mechanisms and help improve the prognostic risk assessment, which have clinical implications for melanoma patients.

An Integrated and Network-Based Analysis of Genes For Aggression in Human and Rodent Models

For most species, aggressive behavior is an evolutionally conserved and genetically specified trait that is essential for survival and fitness. Like many other heritable traits, aggressive behavior has complex genetic bases. Human GWAS studies, genetic mapping and transcriptome analysis of animal models and candidate gene studies in the past decades have provided many useful information for our understanding of genetic architecture underlying aggressive behaviors. However, no genome-wide association has been achieved due to underpowered samples. We present a cross-species and network-based approach to integrate the genetic findings from different types of studies in human and rodent models. We investigated various types of networks formed by these candidate genes, including gene co-expression, protein-protein interaction, canonical pathways and evolutionary profile similarity networks. This analysis helped us to identify top-ranked candidate genes and additional candidates based on their topological importance and the relationship with known candidate genes.

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

Protein function prediction based on data fusion and functional interrelationship

One of the challenging tasks of bioinformatics is to predict more accurate and confident protein functions from genomics and proteomics datasets. Computational approaches use a variety of high throughput experimental data, such as protein-protein interaction (PPI), protein sequences and phylogenetic profiles, to predict protein functions. This paper presents a method that uses transductive multi-label learning algorithm by integrating multiple data sources for classification. Multiple proteomics datasets are integrated to make inferences about functions of unknown proteins and use a directed bi-relational graph to assign labels to unannotated proteins. Our method, bi-relational graph based transductive multi-label function annotation (Bi-TMF) uses functional correlation and topological PPI network properties on both the training and testing datasets to predict protein functions through data fusion of the individual kernel result. The main purpose of our proposed method is to enhance the performance of classifier integration for protein function prediction algorithms. Experimental results demonstrate the effectiveness and efficiency of Bi-TMF on multi-sources datasets in yeast, human and mouse benchmarks. Bi-TMF outperforms other recently proposed methods.

Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

Protein Microarrays in Proteome-wide Applications

Proteomics aims at understanding of protein status under certain physiological or diseased conditions in a large scale. Protein microarray technology has emerged as a promising approach for a wide variety of applications for scientific and clinical research on a whole-proteome scale. Herein, the aim of this review is to summarize the most recent developments in the applications of protein microarrays for biomarker profiling, enzyme substrate profiling, small molecule profiling, protein-protein interaction profiling, and antibody specificity profiling.

Specific ion and buffer effects on protein-protein interactions of a monoclonal antibody.

Better predictive ability of salt and buffer effects on protein-protein interactions requires separating out contributions due to ionic screening, protein charge neutralization by ion binding, and salting-in(out) behavior. We have carried out a systematic study by measuring protein-protein interactions for a monoclonal antibody over an ionic strength range of 25 to 525 mM at 4 pH values (5, 6.5, 8, and 9) in solutions containing sodium chloride, calcium chloride, sodium sulfate, or sodium thiocyante. The salt ions are chosen so as to represent a range of affinities for protein charged and noncharged groups. The results are compared to effects of various buffers including acetate, citrate, phosphate, histidine, succinate, or tris. In low ionic strength solutions, anion binding affinity is reflected by the ability to reduce protein-protein repulsion, which follows the order thiocyanate > sulfate > chloride. The sulfate specific effect is screened at the same ionic strength required to screen the pH dependence of protein-protein interactions indicating sulfate binding only neutralizes protein charged groups. Thiocyanate specific effects occur over a larger ionic strength range reflecting adsorption to charged and noncharged regions of the protein. The latter leads to salting-in behavior and, at low pH, a nonmonotonic interaction profile with respect to sodium thiocyanate concentration. The effects of thiocyanate can not be rationalized in terms of only neutralizing double layer forces indicating the presence of an additional short-ranged protein-protein attraction at moderate ionic strength. Conversely, buffer specific effects can be explained through a charge neutralization mechanism, where buffers with greater valency are more effective at reducing double layer forces at low pH. Citrate binding at pH 6.5 leads to protein charge inversion and the formation of attractive electrostatic interactions. Throughout the report, we highlight similarities in the measured protein-protein interaction profiles with previous studies of globular proteins and of antibodies providing evidence that the behavior will be common to other protein systems.

Long range Debye-Hückel correction for computation of grid-based electrostatic forces between biomacromolecules.

BackgroundBrownian dynamics (BD) simulations can be used to study very large molecular systems, such as models of the intracellular environment, using atomic-detail structures. Such simulations require strategies to contain the computational costs, especially for the computation of interaction forces and energies. A common approach is to compute interaction forces between macromolecules by precomputing their interaction potentials on three-dimensional discretized grids. For long-range interactions, such as electrostatics, grid-based methods are subject to finite size errors. We describe here the implementation of a Debye-Hückel correction to the grid-based electrostatic potential used in the SDA BD simulation software that was applied to simulate solutions of bovine serum albumin and of hen egg white lysozyme.ResultsWe found that the inclusion of the long-range electrostatic correction increased the accuracy of both the protein-protein interaction profiles and the protein diffusion coefficients at low ionic strength.ConclusionsAn advantage of this method is the low additional computational cost required to treat long-range electrostatic interactions in large biomacromolecular systems. Moreover, the implementation described here for BD simulations of protein solutions can also be applied in implicit solvent molecular dynamics simulations that make use of gridded interaction potentials.