pH-selective mutagenesis of protein-protein interfaces: in silico design of therapeutic antibodies with prolonged half-life

Development of a microbial dewaxing agent using three spore forming bacteria

Microbial enhanced oil recovery (MEOR) is a cost effective and efficient method for recovering residual oil. However, the presence of wax (paraffin) in residual oil can substantially reduce the efficiency of MEOR. Therefore, microbial dewaxing is a critical process in MEOR. In this study, a bacterial dewaxing agent of three spore-forming bacteria was developed. Among these bacteria, Bacillus subtilis GZ6 produced the biosurfactant surfactin. Replacing the promoter of the surfactin synthase gene cluster (srfA), increased the titer of surfactin in this strain from 0.33 g/L to 2.32 g/L. The genetically modified strain produced oil spreading rings with diameters increasing from 3.5 ± 0.1 to 4.1 ± 0.2 cm. The LadA F10L/N133R mutant was created by engineering an alkane monooxygenase (LadA) using site-directed mutagenesis in the Escherichia coli host. Compared to the wild-type enzyme, the resulting mutant exhibited an 11.7-fold increase in catalytic efficiency toward the substrate octadecane. When the mutant (pIMPpladA2mu) was expressed in Geobacillus stearothermophilus GZ178 cells, it exhibited a 2.0-fold increase in octadecane-degrading activity. Cultures of the two modified strains (B. subtilis GZ6 (pg3srfA) and G. stearothermophilus GZ178 (pIMPpladA2mu)) were mixed with the culture of Geobacillus thermodenitrificans GZ156 at a ratio of 5:80:15. The resulting composition increased the rate of wax removal by 35% compared to the composition composed of three native strains. This study successfully developed a multi-strain bacterial agent with enhanced oil wax removal capabilities by genetically engineering two bacterial strains.

Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques

The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory activity, making them promising candidates for therapeutic applications. Identifying their binding mode is essential for their development. Experimental structural techniques are effective to get such information, but they are expensive and time-consuming. Here, we propose a computational approach, aiming to identify the epitope of a nanobody that acts as an agonist and a positive allosteric modulator at the rat metabotropic glutamate receptor 5. We employed multiple structure modeling tools, including various artificial intelligence algorithms for epitope mapping. The computationally identified epitope was experimentally validated, confirming the success of our approach. Additional dynamics studies provided further insights on the modulatory activity of the nanobody. The employed methodologies and approaches initiate a discussion on the efficacy of diverse techniques for epitope mapping and later nanobody engineering.

Mechanism Analysis of Antimicrobial Peptide NoPv1 Related to Potato Late Blight through a Computer-Aided Study

Phytophthora infestans (Mont.) de Bary, the oomycotic pathogen responsible for potato late blight, is the most devastating disease of potato production. The primary pesticides used to control oomycosis are phenyl amide fungicides, which cause environmental pollution and toxic residues harmful to both human and animal health. To address this, an antimicrobial peptide, NoPv1, has been screened to target Plasmopara viticola cellulose synthase 2 (PvCesA2) to inhibit the growth of Phytophthora infestans (P. infestans). In this study, we employed AlphaFold2 to predict the three-dimensional structure of PvCesA2 along with NoPv peptides. Subsequently, utilizing computational methods, we dissected the interaction mechanism between PvCesA2 and these peptides. Based on this analysis, we performed a saturation mutation of NoPv1 and successfully obtained the double mutants DP1 and DP2 with a higher affinity for PvCesA2. Meanwhile, dynamics simulations revealed that both DP1 and DP2 utilize a mechanism akin to the barrel-stave model for penetrating the cell membrane. Furthermore, the predicted results showed that the antimicrobial activity of DP1 was superior to that of NoPv1 without being toxic to human cells. These findings may offer insights for advancing the development of eco-friendly pesticides targeting various oomycete diseases, including late blight.

Synthesis, in vitro, and in silico studies of morpholine-based thiosemicarbazones as ectonucleotide pyrophosphatase/phosphodiesterase-1 and -3 inhibitors

An extensive range of new biologically active morpholine based thiosemicarbazones derivatives 3a-r were synthesized, characterized by spectral techniques and evaluated as inhibitors of ENPP isozymes. Most of the novel thiosemicarbazones exhibit potent inhibition towards NPP1 and NPP3 isozymes. Compound 3 h was potent inhibitor of NPP1 with IC50 value of 0.55 ± 0.02. However, the most powerful inhibitor of NPP3 was 3e with an IC50 value of 0.24 ± 0.02. Furthermore, Lineweaver-Burk plot for compound 3 h against NPP1 and for compound 3e against NPP3 was devised through enzymes kinetics studies. Molecular docking and in silico studies was also done for analysis of interaction pattern of all newly synthesized compounds. The results were further validated by molecular dynamic (MD) simulation where the stability of conformational transformation of the best protein-ligand complex (3e) were justified on the basis of RMSD and RMSF analysis.

Acetylphenyl-substituted imidazolium salts: synthesis, characterization, in silico studies and inhibitory properties against some metabolic enzymes

Herein, we present how to synthesize thirteen new 1-(4-acetylphenyl)-3-alkylimidazolium salts by reacting 4-(1-H-imidazol-1-yl)acetophenone with a variety of benzyl halides that contain either electron-donating or electron-withdrawing groups. The structures of the new imidazolium salts were conformed using different spectroscopic methods (1H NMR, 13C NMR, 19F NMR, and FTIR) and elemental analysis techniques. Furthermore, these compounds' the carbonic anhydrase (hCAs) and acetylcholinesterase (AChE) enzyme inhibition activities were investigated. They showed a highly potent inhibition effect toward AChE and hCAs with Ki values in the range of 8.30 ± 1.71 to 120.77 ± 8.61 nM for AChE, 16.97 ± 2.04 to 84.45 ± 13.78 nM for hCA I, and 14.09 ± 2.99 to 69.33 ± 17.35 nM for hCA II, respectively. Most of the synthesized imidazolium salts appeared to be more potent than the standard inhibitor of tacrine (TAC) against AChE and Acetazolamide (AZA) against CA. In the meantime, to prospect for potential synthesized imidazolium salt inhibitor(s) against AChE and hCAs, molecular docking and an ADMET-based approach were exerted.

Surfaces: a software to quantify and visualize interactions within and between proteins and ligands

SUMMARY

Computational methods for the quantification and visualization of the relative contribution of molecular interactions to the stability of biomolecular structures and complexes are fundamental to understand, modulate and engineer biological processes. Here, we present Surfaces, an easy to use, fast and customizable software for quantification and visualization of molecular interactions based on the calculation of surface areas in contact. Surfaces calculations shows equivalent or better correlations with experimental data as computationally expensive methods based on molecular dynamics.

AVAILABILITY AND IMPLEMENTATION

All scripts are available at https://github.com/NRGLab/Surfaces. Surface's documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html.

Assessing Spectral Analysis of Phytoconstituents and Their In Silico Interactions with Target Proteins in Plant Seed Extracts

The pharmacological and preventive attributes of extracts from vegetable seeds have garnered widespread recognition within the scientific community. This study systematically assessed the in vitro antibacterial, antioxidant, and anti-breast cancer properties of phytochemicals present in various solvent-based vegetable seed extracts. We also conducted molecular docking simulations to ascertain their interactions with specific target proteins. Besides, nine distinct chemical constituents were identified using gas chromatography-mass spectrometry (GCMS). Remarkably, the ethyl acetate extract exhibited robust inhibitory effects against Gram-positive and Gram-negative bacterial strains. Furthermore, its capacity for 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging was found to be noteworthy, with an IC50 value of 550.82 ± 1.7 µg/mL, representing a scavenging efficiency of 64.1 ± 2.8%. Additionally, the ethyl acetate extract demonstrated significant hydrogen peroxide (H2O2) scavenging activity, with a maximal scavenging rate of 44.1 ± 1.70% (IC50) at a concentration of 761.17 ± 1.8 µg/mL. Intriguingly, in vitro cytotoxicity assays against human breast cancer (MCF-7) cells revealed varying levels of cell viability at different extract concentrations, suggesting potential anticancer properties. Importantly, these ethyl acetate extracts did not display toxicity to L929 cells across the concentration range tested. Subsequently, we conducted in-silico molecular docking experiments utilizing Discovery Studio 4.0 against the c-Met kinase protein (hepatocyte growth factor; PDB ID: 1N0W). Among the various compounds assessed, 3,4-Dihydroxy-1,6-bis-(3-methoxy-phenyl)-hexa-2,4-diene-1,6-dione exhibited a notable binding energy of -9.1 kcal/mol, warranting further investigation into its potential anticancer properties, clinical applications, and broader pharmacological characteristics.

Evaluation of synthetic aminoquinoline derivatives as urease inhibitors: in vitro, in silico and kinetic studies

Background: Quinoline and acyl thiourea scaffolds have major chemical significance in medicinal chemistry. Quinoline-based acyl thiourea derivatives may potentially target the urease enzyme. Materials & methods: Quinoline-based acyl thiourea derivatives 1-26 were synthesized and tested for urease inhibitory activity. Results: 19 derivatives (1-19) showed enhanced urease enzyme inhibitory potential (IC50 = 1.19-18.92 μM) compared with standard thiourea (IC50 = 19.53 ± 0.032 μM), whereas compounds 20-26 were inactive. Compounds with OCH3, OC2H5, Br and CH3 on the aryl ring showed significantly greater inhibitory potential than compounds with hydrocarbon chains of varying length. Molecular docking studies were conducted to investigate ligand interactions with the enzyme's active site. Conclusion: The identified hits can serve as potential leads against the drug target urease in advanced studies.

Advanced computational analysis of CD40LG variants in atypical X-linked hyper-IgM syndrome

X-Linked Hyper-IgM Syndrome is caused by pathogenic variants in CD40LG. Three patients with atypical clinical and immunological features were identified with variants in CD40LG requiring further characterization. Flow cytometry was used to evaluate CD40L protein expression and binding capacity to a surrogate receptor, CD40-muIg. Though functional anomalies were observed, there was still a lack of clarity regarding the underlying mechanism. We developed structural models for wild-type and the three variants of CD40L protein observed in these patients (p. Lys143Asn, Leu225Ser and Met36Arg) to evaluate structural alterations by molecular mechanic calculations, and assess protein movement by molecular dynamic simulations. These studies demonstrate that functional analysis of variants of unknown significance in CD40LG can be supplemented by advanced computational analysis in atypical clinical contexts. These studies in combination identify the deleterious effects of these variants and potential mechanisms for protein dysfunction.

A short-chain carbonyl reductase mutant is an efficient catalyst in the production of (R)-1,3-butanediol

R-1,3-butanediol (R-1,3-BDO) is an important chiral intermediate of penem and carbapenem synthesis. Among the different synthesis methods to obtain pure enantiomer R-1,3-BDO, oxidation-reduction cascades catalysed by enzymes are promising strategies for its production. Dehydrogenases have been used for the reduction step, but the enantio-selectivity is not high enough for further organic synthesis efforts. Here, a short-chain carbonyl reductase (LnRCR) was evaluated for the reduction step and developed via protein engineering. After docking result analysis with the substrate 4-hydroxy-2-butanone (4H2B), residues were selected for virtual mutagenesis, their substrate-binding energies were compared, and four sites were selected for saturation mutagenesis. High-throughput screening helped identify a Ser154Lys mutant which increased the catalytic efficiency by 115% compared to the parent enzyme. Computer-aided simulations indicated that after single residue replacement, movements in two flexible areas (VTDPAF and SVGFANK) facilitated the volumetric compression of the 4H2B-binding pocket. The number of hydrogen bonds between the stabilized 4H2B-binding pocket of the mutant enzyme and substrate was higher (from four to six) than the wild-type enzyme, while the substrate-binding energy was decreased (from -17.0 kJ/mol to -29.1 kJ/mol). Consequently, the catalytic efficiency increased by approximately 115% and enantio-selectivity increased from 95% to 99%. Our findings indicate that compact and stable substrate-binding pockets are critical for enzyme catalysis. Lastly, the utilization of a microbe expressing the Ser154Lys mutant enzyme was proven to be a robust process to conduct the oxidation-reduction cascade at larger scales.

Harnessing Fc/FcRn Affinity Data from Patents with Different Machine Learning Methods

Monoclonal antibodies are biopharmaceuticals with a very long half-life due to the binding of their Fc portion to the neonatal receptor (FcRn), a pharmacokinetic property that can be further improved through engineering of the Fc portion, as demonstrated by the approval of several new drugs. Many Fc variants with increased binding to FcRn have been found using different methods, such as structure-guided design, random mutagenesis, or a combination of both, and are described in the literature as well as in patents. Our hypothesis is that this material could be subjected to a machine learning approach in order to generate new variants with similar properties. We therefore compiled 1323 Fc variants affecting the affinity for FcRn, which were disclosed in twenty patents. These data were used to train several algorithms, with two different models, in order to predict the affinity for FcRn of new randomly generated Fc variants. To determine which algorithm was the most robust, we first assessed the correlation between measured and predicted affinity in a 10-fold cross-validation test. We then generated variants by in silico random mutagenesis and compared the prediction made by the different algorithms. As a final validation, we produced variants, not described in any patents, and compared the predicted affinity with the experimental binding affinities measured by surface plasmon resonance (SPR). The best mean absolute error (MAE) between predicted and experimental values was obtained with a support vector regressor (SVR) using six features and trained on 1251 examples. With this setting, the error on the log(KD) was less than 0.17. The obtained results show that such an approach could be used to find new variants with better half-life properties that are different from those already extensively used in therapeutic antibody development.

Potential Composite Digenic Contribution of NPC1 and NOD2 Leading to Atypical Lethal Niemann-Pick Type C with Initial Crohn’s Disease-like Presentation: Genotype-Phenotype Correlation Study

Niemann–Pick disease type C (NPC) is an autosomal recessive neurovisceral disease characterized by progressive neurodegeneration with variable involvement of multisystemic abnormalities. Crohn’s disease (CD) is an inflammatory bowel disease (IBD) with a multifactorial etiology influenced by variants in NOD2. Here, we investigated a patient with plausible multisystemic overlapping manifestations of both NPC and CD. Her initial hospitalization was due to a prolonged fever and non-bloody diarrhea. A few months later, she presented with recurrent skin tags and anal fissures. Later, her neurological and pulmonary systems progressively deteriorated, leading to her death at the age of three and a half years. Differential diagnosis of her disease encompassed a battery of clinical testing and genetic investigations. The patient’s clinical diagnosis was inconclusive. Specifically, the histopathological findings were directed towards an IBD disease. Nevertheless, the diagnosis of IBD was not consistent with the patient’s subsequent neurological and pulmonary deterioration. Consequently, we utilized a genetic analysis approach to guide the diagnosis of this vague condition. Our phenotype–genotype association attempts led to the identification of candidate disease-causing variants in both NOD2 and NPC1. In this study, we propose a potential composite digenic impact of these two genes as the underlying molecular etiology. This work lays the foundation for future functional and mechanistic studies to unravel the digenic role of NOD2 and NPC1.

Two physics‐based models for pH ‐dependent calculations of protein solubility

When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.

Vitamin B12 binding to mutated human transcobalamin, in-silico study of TCN2 alanine scanning and ClinVar missense mutations/SNPs

Many missense mutations/SNPs of the TCN2 gene (which yield Transcobalamin (TC)) were reported in the literature but no study is available about their effect on binding to vitamin B12(B12) at the structural level experimentally nor computationally. Predict the effect of TC missense mutations/SNPs on binding affinity to B12 and characterize their contacts to B12 at the structural level. TC-B12 binding energy difference from the wildtype (ΔΔGmut) was calculated for 378 alanine scanning mutations and 76 ClinVar missense mutations, repeated on two distinct X-ray structures of holoTC namely 2BB5 and 4ZRP. Destabilizing mutations then went through 100 ns molecular dynamics simulation to study their effect on TC-B12 binding at the structural level employing 2BB5 structure. Out of the studied 454 mutations (378 alanine mutations + 76 ClinVar mutations), 19 were destabilizing representing 17 amino acid locations. Mutation energy results show a neutral effect on B12 binding of several missense SNPs reported in the literature including I23V, G94S, R215W, P259R, S348F, L376S, and R399Q. Compared to the wildtype, all the destabilizing mutations have higher average RMSD-Ligand in the last 25% of the MD simulation trajectories and lower average hydrogen bond count while the other parameters vary. Previously reported TCN2 SNPs with an unknown effect on TC-B12 binding were found to have a neutral effect in the current study based on mutation energy calculations. Also, we reported 17 possible amino acids that destabilize TC-B12 binding upon mutation (four listed in ClinVar) and studied their structural effect computationally. Communicated by Ramaswamy H. Sarma.

Structure Based Affinity Maturation and Characterizing of SARS-CoV Antibody CR3022 against SARS-CoV-2 by Computational and Experimental Approaches

The COVID-19 epidemic is raging around the world. Neutralizing antibodies are powerful tools for the prevention and treatment of SARS-CoV-2 infection. Antibody CR3022, a SARS-CoV neutralizing antibody, was found to cross-react with SARS-CoV-2, but its affinity was lower than that of its binding with SARS-CoV, which greatly limited the further development of CR3022 against SARS-CoV-2. Therefore, it is necessary to improve its affinity to SARS-CoV-2 in vitro. In this study, the structure-based molecular simulations were utilized to virtually mutate the possible key residues in the complementarity-determining regions (CDRs) of the CR3022 antibody. According to the criteria of mutation energy, the mutation sites that have the potential to impact the antibody affinity were then selected. Then optimized CR3022 mutants with the enhanced affinity were further identified and verified by enzyme-linked immunosorbent assay (ELISA), surface plasma resonance (SPR) and autoimmune reactivity experiments. Finally, molecular dynamics (MD) simulation and binding free energy calculation (MM/PBSA) were performed on the wild-type CR3022 and its two double-site mutants to understand in more detail the contribution of these sites to the higher affinity. It was found that the binding affinity of the CR3022 antibody could be significantly enhanced more than ten times after the introduction of the S103F/Y mutation in HCDR–3 and the S33R mutation in LCDR–1. The additional hydrogen-bonding, hydrophobic interactions, as well as salt-bridges formed between the modified double-site mutated antibody and SARS-CoV-2 RBD were identified. The computational and experimental results clearly demonstrated that the affinity of the modified antibody has been greatly enhanced. This study indicates that CR3022 as a neutralizing antibody recognizing the conserved region of RBD against SARS-CoV with cross-reactivity with SARS-CoV-2, a different member in a large family of coronaviruses, could be improved by the computational and experimental approaches which provided insights for developing antibody drugs against SARS-CoV-2.

Urease inhibitory kinetics, molecular docking, SAR and ADME studies of imine analogues

A series of synthesized imine derivatives (3a–m), including thio-semicarbazone, semicarbazone, thiazole and oxazole functional moieties, were examined for in vitro urease inhibition activity.

Cholesterol Binds in a Reversed Orientation to TCRβ-TM in Which Its OH Group is Localized to the Center of the Lipid Bilayer

T cell receptor (TCR) signaling in response to antigen recognition is essential for the adaptive immune response. Cholesterol keeps TCRs in the resting conformation and mediates TCR clustering by directly binding to the transmembrane domain of the TCRβ subunit (TCRβ-TM), while cholesterol sulfate (CS) displaces cholesterol from TCRβ. However, the atomic interaction of cholesterol or CS with TCRβ remains elusive. Here, we determined the cholesterol and CS binding site of TCRβ-TM in phospholipid bilayers using solution nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulation. Cholesterol binds to the transmembrane residues within a CARC-like cholesterol recognition motif. Surprisingly, the polar OH group of cholesterol is placed in the hydrophobic center of the lipid bilayer stabilized by its polar interaction with K154 of TCRβ-TM. An aromatic interaction with Y158 and hydrophobic interactions with V160 and L161 stabilize this reverse orientation. CS binds to the same site, explaining how it competes with cholesterol. Site-directed mutagenesis of the CARC-like motif disrupted the cholesterol/CS binding to TCRβ-TM, validating the NMR and MD results.

Heparin-binding motif mutations of human diamine oxidase allow the development of a first-in-class histamine-degrading biopharmaceutical

Background

Excessive plasma histamine concentrations cause symptoms in mast cell activation syndrome, mastocytosis, or anaphylaxis. Anti-histamines are often insufficiently efficacious. Human diamine oxidase (hDAO) can rapidly degrade histamine and therefore represents a promising new treatment strategy for conditions with pathological histamine concentrations.

Methods

Positively charged amino acids of the heparin-binding motif of hDAO were replaced with polar serine or threonine residues. Binding to heparin and heparan sulfate, cellular internalization and clearance in rodents were examined.

Results

Recombinant hDAO is rapidly cleared from the circulation in rats and mice. After mutation of the heparin-binding motif, binding to heparin and heparan sulfate was strongly reduced. The double mutant rhDAO-R568S/R571T showed minimal cellular uptake. The short α-distribution half-life of the wildtype protein was eliminated, and the clearance was significantly reduced in rodents.

Conclusions

The successful decrease in plasma clearance of rhDAO by mutations of the heparin-binding motif with unchanged histamine-degrading activity represents the first step towards the development of rhDAO as a first-in-class biopharmaceutical to effectively treat diseases characterized by excessive histamine concentrations in plasma and tissues.

Funding

Austrian Science Fund (FWF) Hertha Firnberg program grant T1135 (EG); Sigrid Juselius Foundation, Medicinska Understödsförening Liv och Hälsa rft (TAS and SeV).

MET Mutation Is a Potential Therapeutic Target for Advanced Endometrial Cancer

Simple Summary

Endometrial cancer is the most common gynecological cancer in developed countries. At initial diagnosis, extra-uterine spread is observed in about 25% of patients. Chemotherapy is the suggested mode of treatment for patients with extra-uterine metastasis; however, the 5-year overall survival rate of these patients remains poor. The development of new therapeutic strategies to improve the poor clinical outcome of patients with advanced endometrial cancer is still in great demand. In this study, we aim to understand the genomic landscape of advanced endometrial cancer and identify new therapeutic targets. Integrated genomic, pathological, and clinical data are analyzed to identify the survival-associated genomic alterations. In addition, the impacts of the genomic alterations are examined in silico, in vitro, and in vivo. The results of this study may aid in developing biomarker-guided treatments for patients with advanced endometrial cancer.

Abstract

An optimal therapeutic regimen for endometrial cancer with extra-uterine metastasis is unavailable. This study aims to improve our understanding of the genomic landscape of advanced endometrial cancer and identify potential therapeutic targets. The clinical and genomic profiles of 81 patients with stage III or IV endometrial cancer were integrated. To identify genomic aberrations associated with clinical outcomes, Cox proportional hazard regression was used. The impacts of the genomic aberrations were validated in vitro and in vivo. The mutation status of MET, U2AF1, BCL9, PPP2R1A, IDH2, CBL, BTK, and CHEK2 were positively correlated with poor clinical outcomes. MET mutations occurred in 30% of the patients who presented with poor overall survival (hazard ratio, 2.606; 95% confidence interval, 1.167~5.819; adjusted p-value, 0.067). Concurrent MET and KRAS mutations presented with the worst outcomes. MET mutations in hepatocyte growth factor (HGF)-binding (58.1%) or kinase (16.2%) domains resulted in differential HGF-induced c-MET phosphorylation. Different types of MET mutations differentially affected tumor growth and displayed different sensitivities to cisplatin and tyrosine kinase inhibitors. MET N375S mutation is a germline variant that causes chemoresistance to cisplatin, with a high incidence in Eastern Asia. This study highlights the ethnic differences in the biology of the disease, which can influence treatment recommendations and the genome-guided clinical trials of advanced endometrial cancer.

Common and unique strategies of myoglobin evolution for deep-sea adaptation of diving mammals

Myoglobin (Mb) is highly concentrated in the myocytes of diving mammals such as whales and seals, in comparison with land animals, and its molecular evolution has played a crucial role in their deep-sea adaptation. We previously resurrected ancestral whale Mbs and demonstrated the evolutional strategies for higher solubility under macromolecular crowding conditions. Pinnipeds, such as seals and sea lions, are also expert diving mammals with Mb-rich muscles. In the present study, we resurrected ancestral pinniped Mbs and investigated their biochemical and structural properties. Comparisons between pinniped and whale Mbs revealed the common and distinctive strategies for the deep-sea adaptation. The overall evolution processes, gaining precipitant tolerance and improving thermodynamic stability, were commonly observed. However, the strategies for improving the folding stability differed, and the pinniped Mbs exploited the shielding of hydrophobic surfaces more effectively than the whale Mbs.

Rational optimization of a human neutralizing antibody of SARS-CoV-2

SARS-CoV-2 has caused a worldwide epidemic of coronavirus disease 19 (COVID-19). Antibody drugs present an effective weapon for tens of millions of COVID-19 patients. Antibodies disrupting the interactions between the receptor-binding domain (RBD) of SARS-CoV-2 S protein and the angiotensin converting enzyme 2 (ACE2) effectively block SARS-CoV-2 cell entry into host cells. In order to rapidly develop more potent neutralizing antibodies, we utilized virtual scanning mutageneses and molecular dynamics simulations to optimize the antibody of P2B-2F6 isolated from single B cells of SARS-CoV-2 infected patients. Two potent P2B-2F6 mutants, namely H:V106R and H:V106R/H:P107Y, were found to possess higher binding affinities with the RBD domain of SARS-CoV-2 than others. Polar interactions are preferred near 106 and 107 paratope residues of the heavy chain. The mutations also increase the hydrogen-bonding network formed between the antibody and the RBD. Notably, the optimized antibodies possess potential neutralizing activity against the alarming SARS-CoV-2 variant of N501Y. This study provides insights into structure-based optimization of antibodies with higher affinity to the antigen. We hope that our proposed antibody mutants could contribute to the development of improved therapies against COVID-19.

Structure-based engineering of pH-dependent antibody binding for selective targeting of solid-tumor microenvironment

Recent development of monoclonal antibodies as mainstream anticancer agents demands further optimization of their safety for use in humans. Potent targeting and/or effector activities on normal tissues is an obvious toxicity concern. Optimization of specific tumor targeting could be achieved by taking advantage of the extracellular acidity of solid tumors relative to normal tissues. Here, we applied a structure-based computational approach to engineer anti-human epidermal growth factor receptor 2 (Her2) antibodies with selective binding in the acidic tumor microenvironment. We used an affinity maturation platform in which dual-pH histidine-scanning mutagenesis was implemented for pH selectivity optimization. Testing of a small set of designs for binding to the recombinant Her2 ectodomain led to the identification of antigen-binding fragment (Fab) variants with the desired pH-dependent binding behavior. Binding selectivity toward acidic pH was improved by as much as 25-fold relative to the parental bH1-Fab. In vitro experiments on cells expressing intact Her2 confirmed that designed variants formatted as IgG1/k full-size antibodies have high affinity and inhibit the growth of tumor spheroids at a level comparable to that of the benchmark anti-Her2 antibody trastuzumab (Herceptin®) at acidic pH, whereas these effects were significantly reduced at physiological pH. In contrast, both Herceptin and the parental bH1 antibody exhibited strong cell binding and growth inhibition irrespective of pH. This work demonstrates the feasibility of computational optimization of antibodies for selective targeting of the acidic environment such as that found in many solid tumors.

Predicting antibody affinity changes upon mutations by combining multiple predictors

Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson’s correlation coefficient between predicted and experimental \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding. Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.

Predicting the Molecular Mechanism of EGFR Domain II Dimer Binding Interface by Machine Learning to Identify Potent Small Molecule Inhibitor for Treatment of Cancer

Epidermal growth factor receptor (EGFR) is a transmembrane druggable target controlling cellular differentiation, proliferation, migration, survival and invasion. EGFR activation mainly occurs by its homo/hetro dimerization molecular phenomenon leading to tumor development and invasion. Several tyrosine kinase based inhibitors were discovered as potent anti-cancer drugs. However, mutations in its kinase domain confer resistance to most of these drugs. To overcome this drug resistance, development of small molecule inhibitors disrupting the EGFR Domain II dimer binding by machine learning methods are promising. Based on this insight, a structure-based drug repurposing strategy was adopted to repurpose the existing FDA approved drugs in blocking the EGFR Domain II mediated dimerization. We identified five best repurposed drug molecules showing good binding affinity at its key arm-cavity dimer interface residues by different machine learning methods. The molecular mechanisms of action of these repurposed drugs were computationally validated by molecular electrostatics potential mapping, point mutations at the dimer arm-cavity binding interface, molecular docking and receptor interaction studies. The present machine learning strategy thus forms the basis of identifying potent and putative small molecule drugs for the treatment of different types of cancer.

Organophosphorus insecticide interacts with the pheromone-binding proteins of Athetis lepigone: Implication for olfactory dysfunction

Athetis lepigone is one of the most severe polyphagous pests, and it has developed resistance to different chemical insecticides. Insects primarily rely on the olfactory system to recognize various environmental chemicals, including xenobiotics such as insecticides. Here, we expressed two A. lepigone pheromone-binding proteins (AlepPBP2 and AlepPBP3), and observed they had higher binding affinities to phoxim than other insecticides, with Ki was 3.30 ± 0.38 μM and 3.27 ± 0.10 μM, respectively. Molecular dynamics simulation, binding mode analysis, and computational alanine scanning showed that six residues (Phe15, Phe39, Ile55, Leu65, Ile97, and Phe122) of AlepPBP2 and three residues (Phe12, Ile52, and Ile134) of AlepPBP3 maybe as potential residues that can change protein ability to bind an organophosphorus insecticide phoxim. Then, we used site-directed mutagenesis assay to mutate these residues into alanine, respectively. Subsequently, the binding assays displayed that Phe15, Phe39, and Ile97 of AlepPBP2, Phe12 and Ile134 of AlepPBP3 caused a significant decrease of AlepPBPs binding ability to phoxim, suggesting they should play crucial roles in the AlepPBPs/phoxim interactions. Our findings could further advance in using PBPs as unique targets to design and develop precise and environmentally-friendly pest control agents with high insecticidal potential using a computer-aided drug design (CADD) approach.

Molecular modelling analysis of T219A mutant envelope protein revealed novel virulence enhancing factors in Dengue virus isolated from Kerala state, India

Dengue virus (DENV) is an emerging health threat and its envelope glycoprotein E, is involved in the anchoring and fusion mechanisms. Anchoring followed by conformational changes of E-protein are responsible for the fusion and entry of DENV into host. The variation in the conformation of the E-protein due to mutations, results in its altered binding with antibodies (Abs) and also its receptors. This leads to failure of neutralization of DENV and enhance the infection. In our earlier studies we have identified T219A mutation in the E-protein of DENV and the present study is focused on the impact of this mutation on the conformation of E-protein and also its binding variation with Abs and Fc-γ receptor. A comparative molecular modelling studies of wild type and T219A mutant E-proteins revealed that, the mutation induced several conformational variations in the E-protein and resulted in the variable binding orientation with altered affinities. Further, the mutation was also observed to enhance the fusion mechanism by Fc-γ receptors that mediate the efficient entry of DENV into host cell through altered membrane fusion mechanism. Such conformational variations of E-protein could be the responsible factors for enhanced virulence of DENV infections.

Dendritic cell biology and its role in tumor immunotherapy

As crucial antigen presenting cells, dendritic cells (DCs) play a vital role in tumor immunotherapy. Taking into account the many recent advances in DC biology, we discuss how DCs (1) recognize pathogenic antigens with pattern recognition receptors through specific phagocytosis and through non-specific micropinocytosis, (2) process antigens into small peptides with proper sizes and sequences, and (3) present MHC-peptides to CD4⁺ and CD8⁺ T cells to initiate immune responses against invading microbes and aberrant host cells. During anti-tumor immune responses, DC-derived exosomes were discovered to participate in antigen presentation. T cell microvillar dynamics and TCR conformational changes were demonstrated upon DC antigen presentation. Caspase-11-driven hyperactive DCs were recently reported to convert effectors into memory T cells. DCs were also reported to crosstalk with NK cells. Additionally, DCs are the most important sentinel cells for immune surveillance in the tumor microenvironment. Alongside DC biology, we review the latest developments for DC-based tumor immunotherapy in preclinical studies and clinical trials. Personalized DC vaccine-induced T cell immunity, which targets tumor-specific antigens, has been demonstrated to be a promising form of tumor immunotherapy in patients with melanoma. Importantly, allogeneic-IgG-loaded and HLA-restricted neoantigen DC vaccines were discovered to have robust anti-tumor effects in mice. Our comprehensive review of DC biology and its role in tumor immunotherapy aids in the understanding of DCs as the mentors of T cells and as novel tumor immunotherapy cells with immense potential.

Dendritic cell biology and its role in tumor immunotherapy

As crucial antigen presenting cells, dendritic cells (DCs) play a vital role in tumor immunotherapy. Taking into account the many recent advances in DC biology, we discuss how DCs (1) recognize pathogenic antigens with pattern recognition receptors through specific phagocytosis and through non-specific micropinocytosis, (2) process antigens into small peptides with proper sizes and sequences, and (3) present MHC-peptides to CD4⁺ and CD8⁺ T cells to initiate immune responses against invading microbes and aberrant host cells. During anti-tumor immune responses, DC-derived exosomes were discovered to participate in antigen presentation. T cell microvillar dynamics and TCR conformational changes were demonstrated upon DC antigen presentation. Caspase-11-driven hyperactive DCs were recently reported to convert effectors into memory T cells. DCs were also reported to crosstalk with NK cells. Additionally, DCs are the most important sentinel cells for immune surveillance in the tumor microenvironment. Alongside DC biology, we review the latest developments for DC-based tumor immunotherapy in preclinical studies and clinical trials. Personalized DC vaccine-induced T cell immunity, which targets tumor-specific antigens, has been demonstrated to be a promising form of tumor immunotherapy in patients with melanoma. Importantly, allogeneic-IgG-loaded and HLA-restricted neoantigen DC vaccines were discovered to have robust anti-tumor effects in mice. Our comprehensive review of DC biology and its role in tumor immunotherapy aids in the understanding of DCs as the mentors of T cells and as novel tumor immunotherapy cells with immense potential.

IL-15Rα membrane anchorage in either cis or trans is required for stabilization of IL-15 and optimal signaling

Interleukin (IL)-15 plays an important role in the communication between immune cells. It delivers its signal through different modes involving three receptor chains: IL-15Rα, IL-2Rβ and IL-2Rγc. The combination of the different chains result in the formation of IL-15Rα/IL-2Rβ/γc trimeric or IL-2Rβ/γc dimeric receptors. In this study, we have investigated the role of the IL-15Rα chain in stabilizing the cytokine in the IL-2Rβ/γc dimeric receptor. By analyzing the key amino acid residues of IL-15 facing IL-2Rβ, we provide evidence of differential interfaces in the presence or in the absence of membrane-anchored IL-15Rα. Moreover, we found that the anchorage of IL-15Rα to the cell surface regardless its mode of presentation - i.e. cis or trans - is crucial for complete signaling. These observations show how the cells can finely modulate the intensity of cytokine signaling through the quality and the level of expression of the receptor chains.

Halogenated derivatives of methotrexate as human dihydrofolate reductase inhibitors in cancer chemotherapy

Methotrexate is a widely used anti-metabolite in cancer chemotherapy. A series of halogenated drugs is designed from Methotrexate to assess their interactions with human dihydrofolate reductase. The aim of this study is to evaluate the performance of the modified drugs compared to the parent Methotrexate. Density Functional Theory is employed to optimize these drugs. Molecular docking calculation of these optimized drugs against dihydrofolate reductase is performed to find out binding affinity. In addition, molecular dynamics simulation is considered for the complexes of best two modified drugs with their receptors. Modifications by the halogens show significant changes in the charge distribution, dipole moment, thermodynamic stability, enthalpy and free energy. The highest binding affinity value (-36.401 KJ/mol) was obtained for M14. Hybrid quantum mechanics/molecular mechanics calculation shows a binding energy of -255.140 KJ/mol. Modified drugs have significant hydrogen and non-covalent bonding interactions with amino acids of the receptor. Molecular dynamics simulation disclosed that the root-mean-square-deviation of the alpha carbon associated with M6-1KMV and M14-1KMV complexes is 2.367 Å and 2.622 Å, respectively. Moreover, the interactions between modified drugs and receptor are mostly persevered in 25 nanosecond molecular dynamics simulation. Ensemble-based docking also confirmed that modified drugs show strong non-bonding interactions with different crystallographic and molecular dynamics based conformers. The best scored drugs show considerable pharmacokinetic properties. Modified derivatives M5, M6, M8, M10, M13 and M14 show the better binding affinity and a good number of hydrogen and other non-bonding interactions with the target protein which are similar to other anticancer drugs.Communicated by Ramaswamy H. Sarma.

Effects of a Novel p.A41P Mutation in the Swine Myogenic factor 5(MYF5) Gene on Protein Stabilizing, Muscle Fiber Characteristics and Meat Quality

Myogenic factor 5 (MYF5) plays an important role in regulating skeletal muscle fiber characteristics, consequently affecting meat production and quality. We identified a novel p.A41P mutation in exon1 of the porcine MYF5 gene by direct sequencing. The mutation was predicted to be destabilizing in protein structure based on the resultant amino acid substitution. We estimated the significant substitution effect of p.A41P on the energy stabilization of Myf5 protein structure. Then, we demonstrated that the mutation in Yorkshire population significantly affected muscle fiber type I composition (p<0.05), loin-eye area of lean meat content (p<0.05) and filter-fluid uptake of meat quality (p<0.01). Furthermore, dominant effects significantly influenced total muscle fiber number (p<0.05). This study suggests that the novel p.A41P mutation in porcine MYF5 may be a valuable genetic marker to affect the muscle fiber characteristics and consequently improve meat production quality and quantity.

Molecular Basis for Necitumumab Inhibition of EGFR Variants Associated with Acquired Cetuximab Resistance

Acquired resistance to cetuximab, an antibody that targets the EGFR, impacts clinical benefit in head and neck, and colorectal cancers. One of the mechanisms of resistance to cetuximab is the acquisition of mutations that map to the cetuximab epitope on EGFR and prevent drug binding. We find that necitumumab, another FDA-approved EGFR antibody, can bind to EGFR that harbors the most common cetuximab-resistant substitution, S468R (or S492R, depending on the amino acid numbering system). We determined an X-ray crystal structure to 2.8 Å resolution of the necitumumab Fab bound to an S468R variant of EGFR domain III. The arginine is accommodated in a large, preexisting cavity in the necitumumab paratope. We predict that this paratope shape will be permissive to other epitope substitutions, and show that necitumumab binds to most cetuximab- and panitumumab-resistant EGFR variants. We find that a simple computational approach can predict with high success which EGFR epitope substitutions abrogate antibody binding. This computational method will be valuable to determine whether necitumumab will bind to EGFR as new epitope resistance variants are identified. This method could also be useful for rapid evaluation of the effect on binding of alterations in other antibody/antigen interfaces. Together, these data suggest that necitumumab may be active in patients who are resistant to cetuximab or panitumumab through EGFR epitope mutation. Furthermore, our analysis leads us to speculate that antibodies with large paratope cavities may be less susceptible to resistance due to mutations mapping to the antigen epitope. Mol Cancer Ther; 17(2); 521-31. ©2017 AACR.

Identification of Strategic Residues at the Interface of Antigen-Antibody Interactions by In Silico Mutagenesis

Structural information pertaining to antigen-antibody interactions is fundamental in immunology, and benefits structure-based vaccine design. Modeling of antigen-antibody immune complexes from co-crystal structures or molecular docking simulations provides an extensive profile of the epitope at the interface; however, the key amino acids involved in the interaction must be further clarified, often through the use of experimental mutagenesis and subsequent binding assays. Here, we describe an in silico mutagenesis method to identify key sites at antigen-antibody interfaces, using significant increase in pH-dependency energy among saturated point mutations. Through a comprehensive analysis of the crystal structures of three antigen-antibody immune complexes, we show that a cutoff value of 1 kcal/mol of increased interaction energy provides good congruency with the experimental non-binding mutations conducted in vitro. This in silico mutagenesis strategy, in association with energy calculations, may provide an efficient tool for antibody-antigen interface analyses, epitope optimization, and/or conformation prediction in structure-based vaccine design.

A pH-dependent computational approach to the effect of mutations on protein stability

This article describes a novel software implementation for high-throughput scanning mutagenesis with a focus on protein stability. The approach combines molecular mechanics calculations with calculations of protein ionization and a Gaussian-chain model of electrostatic interactions in unfolded state. Comprehensive testing demonstrates a state-of-the-art accuracy for predicted free energy differences on single, double, and triple mutations with a correlation coefficient R above 0.7, which takes about 1.5 min per mutation on a single CPU. Unlike most of existing in silico methods for fast mutagenesis, the stability changes are reported as a continuous function of solution pH for wide pH intervals. We also propose a novel in silico strategy for searching stabilized protein variants that is based on combinatorial scanning mutagenesis using representative amino acid types. Our in silico predictions are in excellent agreement with the hyper-stabilized variants of mesophilic cold shock protein found using the Proside method of direct evolution. © 2016 Wiley Periodicals, Inc.

mCSM-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures

Computational methods have traditionally struggled to predict the effect of mutations in antibody–antigen complexes on binding affinity. This has limited their usefulness during antibody engineering and development, and their ability to predict biologically relevant escape mutations. Here we present mCSM-AB, a user-friendly web server for accurately predicting antibody–antigen affinity changes upon mutation which relies on graph-based signatures. We show that mCSM-AB performs better than comparable methods that have been previously used for antibody engineering. mCSM-AB web server is available at http://structure.bioc.cam.ac.uk/mcsm_ab.

AB-Bind: Antibody binding mutational database for computational affinity predictions

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.

A Fab-Selective Immunoglobulin-Binding Domain from Streptococcal Protein G with Improved Half-Life Extension Properties

Background

Half-life extension strategies have gained increasing interest to improve the pharmacokinetic and pharmacodynamic properties of protein therapeutics. Recently, we established an immunoglobulin-binding domain (IgBD) from streptococcal protein G (SpG_C3) as module for half-life extension. SpG_C3 is capable of binding to the Fc region as well as the CH1 domain of Fab arms under neutral and acidic conditions.

Methodology/Principal Findings

Using site-directed mutagenesis, we generated a Fab-selective mutant (SpG_C3Fab) to avoid possible interference with the FcRn-mediated recycling process and improved its affinity for mouse and human IgG by site-directed mutagenesis and phage display selections. In mice, this affinity-improved mutant (SpG_C3FabRR) conferred prolonged plasma half-lives compared with SpG_C3Fab when fused to small recombinant antibody fragments, such as single-chain Fv (scFv) and bispecific single-chain diabody (scDb). Hence, the SpG_C3FabRR domain seems to be a suitable fusion partner for the half-life extension of small recombinant therapeutics.

Conclusions/Significance

The half-life extension properties of SpG_C3 can be retained by restricting binding to the Fab fragment of serum immunoglobulins and can be improved by increasing binding activity. The modified SpG_C3 module should be suitable to extend the half-life of therapeutic proteins and, thus to improve therapeutic activity.

A generic approach to engineer antibody pH-switches using combinatorial histidine scanning libraries and yeast display

There is growing interest in the fast and robust engineering of protein pH-sensitivity that aims to reduce binding at acidic pH, compared to neutral pH. Here, we describe a novel strategy for the incorporation of pH-sensitive antigen binding functions into antibody variable domains using combinatorial histidine scanning libraries and yeast surface display. The strategy allows simultaneous screening for both, high affinity binding at pH 7.4 and pH-sensitivity, and excludes conventional negative selection steps. As proof of concept, we applied this strategy to incorporate pH-dependent antigen binding into the complementary-determining regions of adalimumab. After 3 consecutive rounds of separate heavy and light chain library screening, pH-sensitive variants could be isolated. Heavy and light chain mutations were combined, resulting in 3 full-length antibody variants that revealed sharp, reversible pH-dependent binding profiles. Dissociation rate constants at pH 6.0 increased 230- to 780-fold, while high affinity binding at pH 7.4 in the sub-nanomolar range was retained. Furthermore, binding to huFcRn and thermal stability were not affected by histidine substitutions. Overall, this study emphasizes a generalizable strategy for engineering pH-switch functions potentially applicable to a variety of antibodies and further proteins-based therapeutics.

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).

Computational Tools for Interpreting Ion Channel pH-Dependence

Activity in many biological systems is mediated by pH, involving proton titratable groups with pKas in the relevant pH range. Experimental analysis of pH-dependence in proteins focusses on particular sidechains, often with mutagenesis of histidine, due to its pKa near to neutral pH. The key question for algorithms that predict pKas is whether they are sufficiently accurate to effectively narrow the search for molecular determinants of pH-dependence. Through analysis of inwardly rectifying potassium (Kir) channels and acid-sensing ion channels (ASICs), mutational effects on pH-dependence are probed, distinguishing between groups described as pH-coupled or pH-sensor. Whereas mutation can lead to a shift in transition pH between open and closed forms for either type of group, only for pH-sensor groups does mutation modulate the amplitude of the transition. It is shown that a hybrid Finite Difference Poisson-Boltzmann (FDPB) – Debye-Hückel continuum electrostatic model can filter mutation candidates, providing enrichment for key pH-coupled and pH-sensor residues in both ASICs and Kir channels, in comparison with application of FDPB alone.

On human disease-causing amino acid variants: statistical study of sequence and structural patterns

Statistical analysis was carried out on large set of naturally occurring human amino acid variations, and it was demonstrated that there is a preference for some amino acid substitutions to be associated with diseases. At an amino acid sequence level, it was shown that the disease-causing variants frequently involve drastic changes in amino acid physicochemical properties of proteins such as charge, hydrophobicity, and geometry. Structural analysis of variants involved in diseases and being frequently observed in human population showed similar trends: disease-causing variants tend to cause more changes in hydrogen bond network and salt bridges as compared with harmless amino acid mutations. Analysis of thermodynamics data reported in the literature, both experimental and computational, indicated that disease-causing variants tend to destabilize proteins and their interactions, which prompted us to investigate the effects of amino acid mutations on large databases of experimentally measured energy changes in unrelated proteins. Although the experimental datasets were linked neither to diseases nor exclusory to human proteins, the observed trends were the same: amino acid mutations tend to destabilize proteins and their interactions. Having in mind that structural and thermodynamics properties are interrelated, it is pointed out that any large change in any of them is anticipated to cause a disease.

Protein-protein docking with dynamic residue protonation states

Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc–FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

Protein-protein interactions are fundamental for biological function and are strongly influenced by their local environment. Cellular pH is tightly controlled and is one of the critical environmental factors that regulates protein-protein interactions. Three-dimensional structures of the protein complexes can help us understand the mechanism of the interactions. Since experimental determination of the structures of protein-protein complexes is expensive and time-consuming, computational docking algorithms are helpful to predict the structures. However, none of the current protein-protein docking algorithms account for the critical environmental pH effects. So we developed a pH-sensitive docking algorithm that can dynamically pick the favorable protonation states of the ionizable amino-acid residues. Compared to our previous standard docking algorithm, the new algorithm improves docking accuracy and generates higher-quality predictions over a large dataset of protein-protein complexes. We also use a case study to demonstrate efficacy of the algorithm in predicting a large pH-dependent binding affinity change that cannot be captured by the other methods that neglect pH effects. In principle, the approaches in the study can be used for rational design of pH-dependent protein inhibitors or industrial enzymes that are active over a wide range of pH values.

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.