Probing the effect of point mutations at protein-protein interfaces with free energy calculations

A fully human IgG1 antibody targeting connexin 32 extracellular domain blocks CMTX1 hemichannel dysfunction in an in vitro model

Connexins (Cxs) are fundamental in cell-cell communication, functioning as gap junction channels (GJCs) that facilitate solute exchange between adjacent cells and as hemichannels (HCs) that mediate solute exchange between the cytoplasm and the extracellular environment. Mutations in the GJB1 gene, which encodes Cx32, lead to X-linked Charcot-Marie-Tooth type 1 (CMTX1), a rare hereditary demyelinating disorder of the peripheral nervous system (PNS) without an effective cure or treatment. In Schwann cells, Cx32 HCs are thought to play a role in myelination by enhancing intracellular and intercellular Ca2+ signaling, which is crucial for proper PNS myelination. Single-point mutations (p.S85C, p.D178Y, p.F235C) generate pathological Cx32 HCs characterized by increased permeability ("leaky") or excessive activity ("hyperactive").We investigated the effects of abEC1.1-hIgG1, a fully human immunoglobulin G1 (hIgG1) monoclonal antibody, on wild-type (WT) and mutant Cx32D178Y HCs. Using HeLa DH cells conditionally co-expressing Cx and a genetically encoded Ca2+ biosensor (GCaMP6s), we demonstrated that mutant HCs facilitated 58% greater Ca2+ uptake in response to elevated extracellular Ca2+ concentrations ([Ca2+]ex) compared to WT HCs. abEC1.1-hIgG1 dose-dependently inhibited Ca2+ uptake, achieving a 50% inhibitory concentration (EC50) of ~ 10 nM for WT HCs and ~ 80 nM for mutant HCs. Additionally, the antibody suppressed DAPI uptake and ATP release. An atomistic computational model revealed that serine 56 (S56) of the antibody interacts with aspartate 178 (D178) of WT Cx32 HCs, contributing to binding affinity. Despite the p.D178Y mutation weakening this interaction, the antibody maintained binding to the mutant HC epitope at sub-micromolar concentrations.In conclusion, our study shows that abEC1.1-hIgG1 effectively inhibits both WT and mutant Cx32 HCs, highlighting its potential as a therapeutic approach for CMTX1. These findings expand the antibody's applicability for treating diseases associated with Cx HCs and inform the rational design of next-generation antibodies with enhanced affinity and efficacy against mutant HCs.

Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.

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.

Molecular mechanism of secreted amyloid-β precursor protein in binding and modulating GABABR1a

A recent phenomenal study discovered that the extension domain of secreted amyloid-β precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABA_BR1a) and modulate its synaptic transmission. The work provided an important structural foundation for the modulation of GABA_BR1a; however, the detailed molecular interaction mechanism, crucial for future drug design, remains elusive. Here, we further investigated the dynamical interactions between sAPP peptides and the natively unstructured sushi 1 domain using all-atom molecular dynamics simulations, for both the 17-residue sAPP peptide (APP 17-mer) and its minimally active 9 residue segment (APP 9-mer). We then explored mutations of the APP 9-mer with rigorous free energy perturbation (FEP) calculations. Our in silico mutagenesis studies revealed key residues (D4, W6, and W7) responsible for the binding with the sushi 1 domain. More importantly, one double mutation based on different vertebrate APP sequences from evolution exhibited a stronger binding (ΔΔG = −1.91 ± 0.66 kcal mol⁻¹), indicating a potentially enhanced GABA_BR1a modulator. These large-scale simulations may provide new insights into the binding mechanism between sAPP and the sushi 1 domain, which could open new avenues in the development of future GABA_BR1a-specific therapeutics.

A recent phenomenal study discovered that the extension domain of secreted amyloid-β precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABA_BR1a) and modulate its synaptic transmission.

Exploration of HIV-1 fusion peptide–antibody VRC34.01 binding reveals fundamental neutralization sites

VRC34.01 antibody binding to a vulnerable site of HIV envelope glycoprotein (Env), the gp41 fusion peptide, renders robust HIV neutralization, but several critical mutations decrease binding affinity and result in unbinding.

Saturation Mutagenesis by Efficient Free-Energy Calculation

Single-point mutations in proteins can greatly influence protein stability, binding affinity, protein function or its expression per se. Here, we present accurate and efficient predictions of the free energy of mutation of amino acids. We divided the complete mutational free energy into an uncharging step, which we approximate by a third-power fitting (TPF) approach, and an annihilation step, which we approximate using the one-step perturbation (OSP) method. As a diverse set of test systems, we computed the solvation free energy of all amino acid side chain analogues and obtained an excellent agreement with thermodynamic integration (TI) data. Moreover, we calculated mutational free energies in model tripeptides and established an efficient protocol involving a single reference state. Again, the approximate methods agreed excellently with the TI references, with a root-mean-square error of only 3.6 kJ/mol over 17 mutations. Our combined TPF+OSP approach does show not only a very good agreement but also a 2-fold higher efficiency than full blown TI calculations.

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.

Flexible docking-based molecular dynamics/steered molecular dynamics calculations of protein-protein contacts in a complex of cytochrome P450 1A2 with cytochrome b5

Formation of transient complexes of cytochrome P450 (P450) with another protein of the endoplasmic reticulum membrane, cytochrome b5 (cyt b5), dictates the catalytic activities of several P450s. Therefore, we examined formation and binding modes of the complex of human P450 1A2 with cyt b5. Docking of soluble domains of these proteins was performed using an information-driven flexible docking approach implemented in HADDOCK. Stabilities of the five unique binding modes of the P450 1A2-cyt b5 complex yielded by HADDOCK were evaluated using explicit 10 ns molecular dynamics (MD) simulations in aqueous solution. Further, steered MD was used to compare the stability of the individual P450 1A2-cyt b5 binding modes. The best binding mode was characterized by a T-shaped mutual orientation of the porphyrin rings and a 10.7 Å distance between the two redox centers, thus satisfying the condition for a fast electron transfer. Mutagenesis studies and chemical cross-linking, which, in the absence of crystal structures, were previously used to deduce specific P450-cyt b5 interactions, indicated that the negatively charged convex surface of cyt b5 binds to the positively charged concave surface of P450. Our simulations further elaborate structural details of this interface, including nine ion pairs between R95, R100, R138, R362, K442, K455, and K465 side chains of P450 1A2 and E42, E43, E49, D65, D71, and heme propionates of cyt b5. The universal heme-centric system of internal coordinates was proposed to facilitate consistent classification of the orientation of the two porphyrins in any protein complex.

Binding site preorganization and ligand discrimination in the purine riboswitch

The progress of RNA research has suggested a wide variety of RNA molecules as possible targets for pharmaceutical drug molecules. Structure-based computational methods for predicting binding modes and affinities are now important tools in drug discovery, but these methods have mainly been focused on protein targets. Here we employ molecular dynamics free-energy perturbation calculations and the linear interaction energy method to analyze the energetics of ligand binding to purine riboswitches. Calculations are carried out for 14 different purine complexes with the guanine and adenine riboswitches in order to examine their ligand recognition principles. The simulations yield binding affinities in good agreement with experimental data and rationalize the selectivity of the riboswitches for different ligands. In particular, it is found that these receptors have an unusually high degree of electrostatic preorganization for their cognate ligands, and this effect is further quantified by explicit free-energy calculations, which show that the standard electrostatic linear interaction energy parametrization is suboptimal in this case. The adenine riboswitch specifically uses the electrostatic preorganization to discriminate against guanine by preventing the formation of a G-U wobble base pair.

A multiscale approach to predicting affinity changes in protein-protein interfaces

Substitution mutations in protein-protein interfaces can have a substantial effect on binding, which has consequences in basic and applied biomedical research. Experimental expression, purification, and affinity determination of protein complexes is an expensive and time-consuming means of evaluating the effect of mutations, making a fast and accurate in silico method highly desirable. When the structure of the wild-type complex is known, it is possible to economically evaluate the effect of point mutations with knowledge based potentials, which do not model backbone flexibility, but these have been validated only for single mutants. Substitution mutations tend to induce local conformational rearrangements only. Accordingly, ZEMu (Zone Equilibration of Mutants) flexibilizes only a small region around the site of mutation, then computes its dynamics under a physics-based force field. We validate with 1254 experimental mutants (with 1-15 simultaneous substitutions) in a wide variety of different protein environments (65 protein complexes), and obtain a significant improvement in the accuracy of predicted ΔΔG.

Accessing the applicability of polarized protein-specific charge in linear interaction energy analysis

The reliability of the linear interaction energy (LIE) depends on the atomic charge model used to delineate the Coulomb interaction between the ligand and its environment. In this work, the polarized protein-specific charge (PPC) implementing a recently proposed fitting scheme has been examined in the LIE calculations of the binding affinities for avidin and β-secretase binding complexes. This charge fitting scheme, termed delta restrained electrostatic potential, bypasses the prevalent numerical difficulty of rank deficiency in electrostatic-potential-based charge fitting methods via a dual-step fitting strategy. A remarkable consistency between the predicted binding affinities and the experimental measurement has been observed. This work serves as a direct evidence of PPC's applicability in rational drug design.

Insights into AT1 receptor activation through AngII binding studies

This study investigates the binding of angiotensin II (AngII) to the angiotensin II type 1 receptor (AT1R), taking into consideration several known activation elements that have been observed for G-protein-coupled receptors (GPCRs). In order to determine the crucial interactions of AngII upon binding, several MD simulations were implemented using AngII conformations derived from experimental data (NMR ROEs) and in silico flexible docking methodologies. An additional goal was to simulate the induced activation mechanism and examine the already known structural rearrangements of GPCRs upon activation. Performing MD simulations to the AT1R - AngII - lipids complex, a series of dynamic changes in the topology of AngII and the intracellular part of the receptor were observed. Overall, the present study proposes a complete binding profile of AngII to the AT1R, as well as the key transitional elements of the receptor and the agonist peptide upon activation through NMR and in silico studies.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

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

Understanding the effects of mutation on pH-dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein–protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH-dependent energy profiles demonstrate excellent agreement with experimentally measured pH-dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH-dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH-selective binding. The method can be used to select mutations that change the pH-dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH-dependent binding behavior of IgG to FcRn with the aim of improving the half-life of therapeutic antibodies in the target organism.

Comprehensive interrogation of natural TALE DNA-binding modules and transcriptional repressor domains

Transcription activator-like effectors (TALE) are sequence-specific DNA binding proteins that harbor modular, repetitive DNA binding domains. TALEs have enabled the creation of customizable designer transcriptional factors and sequence-specific nucleases for genome engineering. Here we report two improvements of the TALE toolbox for achieving efficient activation and repression of endogenous gene expression in mammalian cells. We show that the naturally occurring repeat variable diresidue (RVD) Asn-His (NH) has high biological activity and specificity for guanine, a highly prevalent base in mammalian genomes. We also report an effective TALE transcriptional repressor architecture for targeted inhibition of transcription in mammalian cells. These findings will improve the precision and effectiveness of genome engineering that can be achieved using TALEs.

Molecular interactions of c-ABL mutants in complex with imatinib/nilotinib: a computational study using linear interaction energy (LIE) calculations

In spite of the effectiveness of Imatinib for chronic myeloid leukemia (CML) treatment, resistance has repeatedly been reported and is associated with point mutations in the BCR-ABL chimeric gene. To overcome this resistance, several inhibitors of BCR-ABL tyrosine kinase activity were developed. In this context, computational simulations have become a powerful tool for understanding drug-protein interactions. Herein, we report a comparative molecular dynamics analysis of the interaction between two tyrosine kinase inhibitors (imatinib or nilotinib) against wild type c-ABL protein and 12 mutants, using the semi-empirical linear interaction energy (LIE) method, to assess the feasibility of this approach for studying resistance against the inhibitory activity of these drugs. In addition, to understand the structural changes that are associated with resistance, we describe the behavior of water molecules that interact simultaneously with specific residues (Glu286, Lys271 and Asp381) of c-ABL (wild type or mutant) and their relationship with drug resistance. Experimental IC50 values for the interaction between imatinib, wild type c-ABL, and 12 mutants were used to obtain the proper LIE coefficients (α, β and γ) to estimate the free energy of the binding of imatinib with wild-type and mutant proteins, and values were extrapolated for the analysis of the nilotinib/c-ABL interaction. Our results indicate that LIE was suitable to predict the superior inhibitory activity of nilotinib and the resistance to inhibition that was observed in c-ABL mutants. Additionally, for c-ABL mutants, the observed number of water molecules being turned over while interacting with amino acids Glu286, Lys271 and Asp381 was associated with resistance to imatinib, resulting in a less effective inhibition of the kinase activity.

Free-energy simulations reveal that both hydrophobic and polar interactions are important for influenza hemagglutinin antibody binding

Antibodies binding to conserved epitopes can provide a broad range of neutralization to existing influenza subtypes and may also prevent the propagation of potential pandemic viruses by fighting against emerging strands. Here we propose a computational framework to study structural binding patterns and detailed molecular mechanisms of viral surface glycoprotein hemagglutinin (HA) binding with a broad spectrum of neutralizing monoclonal antibody fragments (Fab). We used rigorous free-energy perturbation (FEP) methods to calculate the antigen-antibody binding affinities, with an aggregate underlying molecular-dynamics simulation time of several microseconds (∼2 μs) using all-atom, explicit-solvent models. We achieved a high accuracy in the validation of our FEP protocol against a series of known binding affinities for this complex system, with <0.5 kcal/mol errors on average. We then introduced what to our knowledge are novel mutations into the interfacial region to further study the binding mechanism. We found that the stacking interaction between Trp-21 in HA2 and Phe-55 in the CDR-H2 of Fab is crucial to the antibody-antigen association. A single mutation of either W21A or F55A can cause a binding affinity decrease of ΔΔG > 4.0 kcal/mol (equivalent to an ∼1000-fold increase in the dissociation constant K(d)). Moreover, for group 1 HA subtypes (which include both the H1N1 swine flu and the H5N1 bird flu), the relative binding affinities change only slightly (< ±1 kcal/mol) when nonpolar residues at the αA helix of HA mutate to conservative amino acids of similar size, which explains the broad neutralization capability of antibodies such as F10 and CR6261. Finally, we found that the hydrogen-bonding network between His-38 (in HA1) and Ser-30/Gln-64 (in Fab) is important for preserving the strong binding of Fab against group 1 HAs, whereas the lack of such hydrogen bonds with Asn-38 in most group 2 HAs may be responsible for the escape of antibody neutralization. These large-scale simulations may provide new insight into the antigen-antibody binding mechanism at the atomic level, which could be essential for designing more-effective vaccines for influenza.

Linear interaction energy: method and applications in drug design

A broad range of computational methods exist for the estimation of ligand-protein binding affinities. In this chapter we will provide a guide to the linear interaction energy (LIE) method for binding free energy calculations, focusing on the drug design problem. The method is implemented in combination with molecular dynamics (MD) sampling of relevant conformations of the ligands and complexes under consideration. The detailed procedure for MD sampling is followed by key notes in order to properly analyze such sampling and obtain sufficiently accurate estimations of ligand-binding affinities.

The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

Background

Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need.

Results

In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes.

Conclusion

Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.

A multiscale systems perspective on cancer, immunotherapy, and Interleukin-12

Monoclonal antibodies represent some of the most promising molecular targeted immunotherapies. However, understanding mechanisms by which tumors evade elimination by the immune system of the host presents a significant challenge for developing effective cancer immunotherapies. The interaction of cancer cells with the host is a complex process that is distributed across a variety of time and length scales. The time scales range from the dynamics of protein refolding (i.e., microseconds) to the dynamics of disease progression (i.e., years). The length scales span the farthest reaches of the human body (i.e., meters) down to the range of molecular interactions (i.e., nanometers). Limited ranges of time and length scales are used experimentally to observe and quantify changes in physiology due to cancer. Translating knowledge obtained from the limited scales observed experimentally to predict patient response is an essential prerequisite for the rational design of cancer immunotherapies that improve clinical outcomes. In studying multiscale systems, engineers use systems analysis and design to identify important components in a complex system and to test conceptual understanding of the integrated system behavior using simulation. The objective of this review is to summarize interactions between the tumor and cell-mediated immunity from a multiscale perspective. Interleukin-12 and its role in coordinating antibody-dependent cell-mediated cytotoxicity is used illustrate the different time and length scale that underpin cancer immunoediting. An underlying theme in this review is the potential role that simulation can play in translating knowledge across scales.

Computer applications for prediction of protein-protein interactions and rational drug design

In recent years, protein-protein interactions are becoming the object of increasing attention in many different fields, such as structural biology, molecular biology, systems biology, and drug discovery. From a structural biology perspective, it would be desirable to integrate current efforts into the structural proteomics programs. Given that experimental determination of many protein-protein complex structures is highly challenging, and in the context of current high-performance computational capabilities, different computer tools are being developed to help in this task. Among them, computational docking aims to predict the structure of a protein-protein complex starting from the atomic coordinates of its individual components, and in recent years, a growing number of docking approaches are being reported with increased predictive capabilities. The improvement of speed and accuracy of these docking methods, together with the modeling of the interaction networks that regulate the most critical processes in a living organism, will be essential for computational proteomics. The ultimate goal is the rational design of drugs capable of specifically inhibiting or modifying protein-protein interactions of therapeutic significance. While rational design of protein-protein interaction inhibitors is at its very early stage, the first results are promising.

Free energy simulations reveal a double mutant avian H5N1 virus hemagglutinin with altered receptor binding specificity

Historically, influenza pandemics have been triggered when an avian influenza virus or a human/avian reassorted virus acquires the ability to replicate efficiently and become transmissible in the human population. Most critically, the major surface glycoprotein hemagglutinin (HA) must adapt to the usage of human-like (alpha-2,6-linked) sialylated glycan receptors. Therefore, identification of mutations that can switch the currently circulating H5N1 HA receptor binding specificity from avian to human might provide leads to the emergence of pandemic H5N1 viruses. To define such mutations in the H5 subtype, here we provide a computational framework that combines molecular modeling with extensive free energy simulations. Our results show that the simulated binding affinities are in good agreement with currently available experimental data. Moreover, we predict that one double mutation (V135S and A138S) in HA significantly enhances alpha-2,6-linked receptor recognition by the H5 subtype. Our simulations indicate that this double mutation in H5N1 HA increases the binding affinity to alpha-2,6-linked sialic acid receptors by 2.6 +/- 0.7 kcal/mol per HA monomer that primarily arises from the electrostatic interactions. Further analyses reveal that introduction of this double mutation results in a conformational change in the receptor binding pocket of H5N1 HA. As a result, a major rearrangement occurs in the hydrogen-bonding network of HA with the human receptor, making the human receptor binding pattern of double mutant H5N1 HA surprisingly similar to that observed in human H1N1 HA. These large scale molecular simulations on single and double mutants thus provide new insights into our understanding toward human adaptation of the avian H5N1 virus.

Molecular dynamics simulations reveal that Tyr-317 phosphorylation reduces Shc binding affinity for phosphotyrosyl residues of epidermal growth factor receptor

The Src homology 2 (SH2) and collagen domain protein Shc plays a pivotal role in signaling via tyrosine kinase receptors, including epidermal growth factor receptor (EGFR). Shc binding to phospho-tyrosine residues on activated receptors is mediated by the SH2 and phospho-tyrosine binding (PTB) domains. Subsequent phosphorylation on Tyr-317 within the Shc linker region induces Shc interactions with Grb2-Son of Sevenless that initiate Ras-mitogen-activated protein kinase signaling. We use molecular dynamics simulations of full-length Shc to examine how Tyr-317 phosphorylation controls Shc conformation and interactions with EGFR. Our simulations reveal that Shc tyrosine phosphorylation results in a significant rearrangement of the relative position of its domains, suggesting a key conformational change. Importantly, computational estimations of binding affinities show that EGFR-derived phosphotyrosyl peptides bind with significantly more strength to unphosphorylated than to phosphorylated Shc. Our results unveil what we believe is a novel structural phenomenon, i.e., tyrosine phosphorylation of Shc within its linker region regulates the binding affinity of SH2 and PTB domains for phosphorylated Shc partners, with important implications for signaling dynamics.

Empirical estimation of the energetic contribution of individual interface residues in structures of protein-protein complexes

We report a simple algorithm to scan interfaces in protein-protein complexes for identifying binding 'hot spots'. The change in side-chain solvent accessible area (DeltaASA) of interface residues has been related to change in binding energy due to mutating interface residues to Ala (DeltaDeltaG (X --> ALA)) based on two criteria-hydrogen bonding across the interface and location in the interface core-both of which are major determinants in specific, high-affinity binding. These relationships are used to predict the energetic contribution of individual interface residues. The predictions are tested against 462 experimental X --> ALA mutations from 28 interfaces with an average unsigned error of 1.04 kcal/mol. More than 80% of interface hot spots (with experimental DeltaDeltaG > or = 2 kcal/mol) could be identified as being energetically important. From the experimental values, Asp, Lys, Tyr and Trp are found to contribute most of the binding energy, burying >45 A2 on average. The method described here would be useful to understand and interfere with protein interactions by assessing the energetic importance of individual interface residues.

Understanding ligand-protein interactions in affinity membrane chromatography for antibody purification

Affinity chromatography with Protein A beads has become the conventional unit operation for the primary capture of monoclonal antibodies. However, Protein A activated supports are expensive and ligand leakage is an issue to be considered. In addition, the limited production capabilities of the chromatographic process drive the research towards feasible alternatives. The use of synthetic ligands as Protein A substitutes has been considered in this work. Synthetic ligands, that mimic the interaction between Protein A and the constant fragment (Fc) of immunoglobulins, have been immobilized on cellulosic membrane supports. The resulting affinity membranes have been experimentally characterized with pure immunoglobulin G (IgG). The effects of the membrane support and of the spacer arm on the ligand-ligate interaction have been studied in detail. Experimental data have been compared with molecular dynamic simulations with the aim of better understanding the interaction mechanisms. Molecular dynamic simulations were performed in explicit water, modelling the membrane as a matrix of overlapped glucopyranose units. Electrostatic charges of the ligand and spacer were calculated through ab initio methods to complete the force field used to model the membrane. The simulations enabled to elucidate how the interactions of surface, spacer and ligand with IgG, contribute to the formation of the bond between protein and affinity membrane.

Single mutation induced H3N2 hemagglutinin antibody neutralization: a free energy perturbation study

The single mutation effect on the binding affinity of H3N2 viral protein hemagglutinin (HA) with the monoclonical antibody fragment (Fab) is studied in this paper using the free energy perturbation (FEP) simulations. An all-atom protein model with explicit solvents is used to perform an aggregate of several microsecond FEP molecular dynamics simulations. A recent experiment shows that a single mutation in H3N2 HA, T131I, increases the antibody-antigen dissociation constant Kd by a factor of approximately 4000 (equivalent to a binding affinity decrease of approximately 5 kcal/mol), thus introducing an escape of the antibody (Ab) neutralization. Our FEP result confirms this experimental finding by estimating the HA-Ab binding affinity decrease of 5.2 +/- 0.9 kcal/mol but with a somewhat different molecular mechanism from the experimental findings. Detailed analysis reveals that this large binding affinity decrease in the T131I mutant is mainly due to the displacement of two bridge water molecules otherwise present in the wild-type HA/Ab interface. The decomposition of the binding free energy supports this observation, as the major contribution to the binding affinity is from the electrostatic interactions. In addition, we find that the loss of the binding affinity is also related to the large conformational distortion of one loop (loop 155-161) in the unbound state of the mutant. We then simulate all other possible mutations for this specific mutation site T131, and predict a few more mutations with even larger decreases in the binding affinity (i.e., better candidates for antibody neutralization), such as T131W, T131Y, and T131F. As for further validation, we have also modeled another mutation, S157L, with experimental binding affinity available (Kd increasing approximately 500 times), and found a binding affinity decrease of 4.1 +/- 1.0 kcal/mol, which is again in excellent agreement with experiment. These large scale simulations might provide new insights into the detailed physical interaction, possible future escape mutation, and antibody-antigen coevolution relationship between influenza virus and human antibodies.

A computational modeling and molecular dynamics study of the Michaelis complex of human protein Z-dependent protease inhibitor (ZPI) and factor Xa (FXa)

Protein Z-dependent protease inhibitor (ZPI) and antithrombin III (AT3) are members of the serpin superfamily of protease inhibitors that inhibit factor Xa (FXa) and other proteases in the coagulation pathway. While experimental structural information is available for the interaction of AT3 with FXa, at present there is no structural data regarding the interaction of ZPI with FXa, and the precise role of this interaction in the blood coagulation pathway is poorly understood. In an effort to gain a structural understanding of this system, we have built a solvent equilibrated three-dimensional structural model of the Michaelis complex of human ZPI/FXa using homology modeling, protein-protein docking and molecular dynamics simulation methods. Preliminary analysis of interactions at the complex interface from our simulations suggests that the interactions of the reactive center loop (RCL) and the exosite surface of ZPI with FXa are similar to those observed from X-ray crystal structure-based simulations of AT3/FXa. However, detailed comparison of our modeled structure of ZPI/FXa with that of AT3/FXa points to differences in interaction specificity at the reactive center and in the stability of the inhibitory complex, due to the presence of a tyrosine residue at the P1 position in ZPI, instead of the P1 arginine residue in AT3. The modeled structure also shows specific structural differences between AT3 and ZPI in the heparin-binding and flexible N-terminal tail regions. Our structural model of ZPI/FXa is also compatible with available experimental information regarding the importance for the inhibitory action of certain basic residues in FXa.

Identification of hot-spot residues in protein-protein interactions by computational docking

BACKGROUND

The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.

RESULTS

We have applied here normalized interface propensity (NIP) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex.

CONCLUSION

The NIP values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.

Protein-protein docking and analysis reveal that two homologous bacterial adenylyl cyclase toxins interact with calmodulin differently

Calmodulin (CaM), a eukaryotic calcium sensor that regulates diverse biological activities, consists of N- and C-terminal globular domains (N-CaM and C-CaM, respectively). CaM serves as the activator of CyaA, a 188-kDa adenylyl cyclase toxin secreted by Bordetella pertussis, which is the etiologic agent for whooping cough. Upon insertion of the N-terminal adenylyl cyclase domain (ACD) of CyaA to its targeted eukaryotic cells, CaM binds to this domain tightly ( approximately 200 pm affinity). This interaction activates the adenylyl cyclase activity of CyaA, leading to a rise in intracellular cAMP levels to disrupt normal cellular signaling. We recently solved the structure of CyaA-ACD in complex with C-CaM to elucidate the mechanism of catalytic activation. However, the structure of the interface between N-CaM and CyaA, the formation of which contributes a 400-fold increase of binding affinity between CyaA and CaM, remains elusive. Here, we used site-directed mutations and molecular dynamic simulations to generate several working models of CaM-bound CyaA-ACD. The validity of these models was evaluated by disulfide bond cross-linking, point mutations, and fluorescence resonance energy transfer experiments. Our study reveals that a beta-hairpin region (amino acids 259-273) of CyaA-ACD likely makes contacts with the second calcium binding motif of the extended CaM. This mode of interaction differs from the interaction of N-CaM with anthrax edema factor, which binds N-CaM via its helical domain. Thus, two structurally conserved, bacterial adenylyl cyclase toxins have evolved to utilize distinct binding surfaces and modes of activation in their interaction with CaM, a highly conserved eukaryotic signaling protein.

Prediction of T-cell epitope

The prevailing methods to predict T-cell epitopes are reviewed. Motif matching, matrix, support vector machine (SVM), and empirical scoring function methods are mainly reviewed; and the thermodynamic integration (TI) method using all-atom molecular dynamics (MD) simulation is mentioned briefly. The motif matching method appeared first and developed with the increased understanding of the characteristic structure of MHC-peptide complexes, that is, pockets aligned in the groove and corresponding residues fitting on them. This method is now becoming outdated due to the insufficiency and inaccuracy of information. The matrix method, the generalization of interaction between pockets of MHC and residues of bound peptide to all the positions in the groove, is the most prevalent one. Efficiency of calculation makes this method appropriate to scan for candidates of T-cell epitopes within whole expressed proteins in an organ or even in a body. A large amount of experimental binding data is necessary to determine a matrix. SVM is a relative of the artificial neural network, especially direct generalization of a linear Perceptron. By incorporating non-binder data and adopting encoding that reflects the physical properties of amino acids, its performance becomes quite high. Empirical scoring functions apparently seem to be founded on a physical basis. However, the estimates directly derived from the method using only structural data are far from practical use. Through regression with binding data of a series of ligands and receptors, this method predicts binding affinity with appropriate accuracy. The TI method using MD requires only structural data and a general atomic parameter, that is, force field, and hence theoretically most consistent; however, the extent of perturbation, inaccuracy of the force field, the necessity of an immense amount of calculations, and continued difficulty of sampling an adequate structure hamper the application of this method in practical use.

Testing the Coulomb/Accessible Surface Area solvent model for protein stability, ligand binding, and protein design

Background

Protein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent. We continue to evaluate the performance of the Coulomb/Accessible Surface Area (CASA) implicit solvent model, in combination with the Charmm19 molecular mechanics force field. We test a set of model parameters optimized earlier, and we also carry out a new optimization in this work, using as a target a set of experimental stability changes for single point mutations of various proteins and peptides. The optimization procedure is general, and could be used with other force fields. The computation of stability changes requires a model for the unfolded state of the protein. In our approach, this state is represented by tripeptide structures of the sequence Ala-X-Ala for each amino acid type X. We followed an iterative optimization scheme which, at each cycle, optimizes the solvation parameters and a set of tripeptide structures for the unfolded state. This protocol uses a set of 140 experimental stability mutations and a large set of tripeptide conformations to find the best tripeptide structures and solvation parameters.

Results

Using the optimized parameters, we obtain a mean unsigned error of 2.28 kcal/mol for the stability mutations. The performance of the CASA model is assessed by two further applications: (i) calculation of protein-ligand binding affinities and (ii) computational protein design. For these two applications, the previous parameters and the ones optimized here give a similar performance. For ligand binding, we obtain reasonable agreement with a set of 55 experimental mutation data, with a mean unsigned error of 1.76 kcal/mol with the new parameters and 1.47 kcal/mol with the earlier ones. We show that the optimized CASA model is not inferior to the Generalized Born/Surface Area (GB/SA) model for the prediction of these binding affinities. Likewise, the new parameters perform well for the design of 8 SH3 domain proteins where an average of 32.8% sequence identity relative to the native sequences was achieved. Further, it was shown that the computed sequences have the character of naturally-occuring homologues of the native sequences.

Conclusion

Overall, the two CASA variants explored here perform very well for a wide variety of applications. Both variants provide an efficient solvent treatment for the computational engineering of ligands and proteins.

A Model for How Ribosomal Release Factors Induce Peptidyl-tRNA Cleavage in Termination of Protein Synthesis

A major unresolved question in messenger RNA translation is how ribosomal release factors terminate protein synthesis. Class 1 release factors decode stop codons and trigger hydrolysis of the bond between the nascent polypeptide and tRNA some 75 A away from the decoding site. While the gross features of the release factor-ribosome interaction have been revealed by low-resolution crystal structures, there is no information on the atomic level at either the decoding or peptidyl transfer center. We used extensive computer simulations, constrained by experimental data, to predict how bacterial release factors induce peptide dissociation from the ribosome. A distinct structural solution is presented for how the methylated Gln residue of the universally conserved GGQ release factor motif inserts into the ribosomal A site and promotes rapid reaction with the peptidyl-tRNA substrate. This model explains key mutation experiments and shows that the ribosomal peptidyl transfer center catalyzes its two chemical reactions by a common mechanism.

Free energy simulations of uncatalyzed DNA replication fidelity: structure and stability of T.G and dTTP.G terminal DNA mismatches flanked by a single dangling nucleotide

A reference system for DNA replication fidelity was studied by free energy perturbation (FEP) and linear interaction energy (LIE) methods. The studied system included a hydrated duplex DNA with the 5'-CG dangling end of the templating strand, and dCTP4-.Mg2+ or dTTP4-.Mg2+ inserted opposite the dangling G to form a correct (i.e., Watson-Crick) or incorrect (i.e., wobble) base pair, respectively. The average distance between the 3'-terminal oxygen of the primer strand and the alpha-phosphorus of dNTP was found to be 0.2 A shorter for the correct base pair than for the incorrect base pair. Binding of the incorrect dNTP was found to be disfavored by 0.4 kcal/mol relative to the correct dNTP. We estimated that improved binding and more near-attack configurations sampled by the correct base pair should translate in aqueous solution and in the absence of DNA polymerase into a six times faster rate for the incorporation of the correct dNTP into DNA. The accuracy of the calculated binding free energy difference was verified by examining the relative free energy for melting duplex DNA containing GC and GT terminal base pairs flanked by a 5' dangling C. The calculated LIE and FEP free energies of 1.7 and 1.1 kcal/mol, respectively, compared favorably with the experimental estimate of 1.4 kcal/mol obtained using the nearest neighbor parameters. To decompose the calculated free energies into additive electrostatic and van der Waals contributions and to provide a set of rigorous theoretical data for the parametrization of the LIE method, we suggested a variant of the FEP approach, for which we coined a binding-relevant free energy (BRFE) acronym. BRFE approach is characterized by its unique perturbation pathway and by its exclusion of the intramolecular energy of a rigid part of the ligand from the total potential energy.

In silico screening of mutational effects on enzyme-proteic inhibitor affinity: a docking-based approach

Background

Molecular recognition between enzymes and proteic inhibitors is crucial for normal functioning of many biological pathways. Mutations in either the enzyme or the inhibitor protein often lead to a modulation of the binding affinity with no major alterations in the 3D structure of the complex.

Results

In this study, a rigid body docking-based approach has been successfully probed in its ability to predict the effects of single and multiple point mutations on the binding energetics in three enzyme-proteic inhibitor systems. The only requirement of the approach is an accurate structural model of the complex between the wild type forms of the interacting proteins, with the assumption that the architecture of the mutated complexes is almost the same as that of the wild type and no major conformational changes occur upon binding. The method was applied to 23 variants of the ribonuclease inhibitor-angiogenin complex, to 15 variants of the barnase-barstar complex, and to 8 variants of the bovine pancreatic trypsin inhibitor-β Trypsin system, leading to thermodynamic and kinetic estimates consistent with in vitro data. Furthermore, simulations with and without explicit water molecules at the protein-protein interface suggested that they should be included in the simulations only when their positions are well defined both in the wild type and in the mutants and they result to be relevant for the modulation of mutational effects on the association process.

Conclusion

The correlative models built in this study allow for predictions of mutational effects on the thermodynamics and kinetics of association of three substantially different systems, and represent important extensions of our computational approach to cases in which it is not possible to estimate the absolute free energies. Moreover, this study is the first example in the literature of an extensive evaluation of the correlative weights of the single components of the ZDOCK score on the thermodynamics and kinetics of binding of protein mutants compared to the native state.

Finally, the results of this study corroborate and extend a previously developed quantitative model for in silico predictions of absolute protein-protein binding affinities spanning a wide range of values, i.e. from -10 up to -21 kcal/mol.

The computational approach is simple and fast and can be used for structure-based design of protein-protein complexes and for in silico screening of mutational effects on protein-protein recognition.

Energetics of codon-anticodon recognition on the small ribosomal subunit

Recent crystal structures of the small ribosomal subunit have made it possible to examine the detailed energetics of codon recognition on the ribosome by computational methods. The binding of cognate and near-cognate anticodon stem loops to the ribosome decoding center, with mRNA containing the Phe UUU and UUC codons, are analyzed here using explicit solvent molecular dynamics simulations together with the linear interaction energy (LIE) method. The calculated binding free energies are in excellent agreement with experimental binding constants and reproduce the relative effects of mismatches in the first and second codon position versus a mismatch at the wobble position. The simulations further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with the Phe UUU codon. It is also found that the ribosome significantly enhances the intrinsic stability differences of codon-anticodon complexes in aqueous solution. Structural analysis of the simulations confirms the previously suggested importance of the universally conserved nucleotides A1492, A1493, and G530 in the decoding process.

Exploring the molecular basis of the enantioselective binding of penicillin G acylase towards a series of 2-aryloxyalkanoic acids: A docking and molecular dynamics study

In the present paper, molecular modeling studies were undertaken in order to shed light on the molecular basis of the observed enantioselectivity of penicillin G acylase (PGA), a well known enzyme for its industrial applications, towards 16 racemic 2-aryloxyalkanoic acids, which have been reported to affect several biological systems. With this intention docking calculations and MD simulations were performed. Docking results indicated that the (S)-enantiomers establish several electrostatic interactions with SerB1, SerB386 and ArgB263 of PGA. Conversely, the absence of specific polar interactions between the (R)-enantiomers and ArgB263 seems to be the main reason for the different binding affinities observed between the two enantiomers. Results of molecular dynamics simulations demonstrated that polar interactions are responsible for both the ligand affinity and PGA enantiospecificity. Modeling calculations provided possible explanations for the observed enantioselectivity of the enzyme that rationalize available experimental data and could be the basis for future protein engineering efforts.

Free energy calculations show that acidic P1 variants undergo large pKa shifts upon binding to trypsin

Serine proteinases and their protein inhibitors belong to one of the most comprehensively studied models of protein-protein interactions. It is well established that the narrow trypsin specificity is caused by the presence of a negatively charged aspartate at the specificity pocket. X-ray crystallography as well as association measurements revealed, surprisingly, that BPTI with glutamatic acid as the primary binding (P1) residue was able to bind to trypsin. Previous free energy calculations showed that there was a substantially unfavorable binding free energy associated with accommodation of ionized P1 Glu at the S1-site of trypsin. In this study, the binding of P1 Glu to trypsin has been systematically investigated in terms of the protonation states of P1 Glu and Asp189, the orientation of Gln192, as well as the possible presence of counterions using the linear interaction energy (LIE) approach and the free energy perturbation (FEP) method. Twenty-four conceivable binding arrangements were evaluated and quantitative agreement with experiments is obtained when the P1 Glu binds in its protonated from. The results suggest that P1 Glu is one of the variants of BPTI that inhibit trypsin strongest at low pH, contrary to the specificity profile of trypsin, suggesting a new regulation mechanism of trypsin-like enzymes.

Predicting proteinase specificities from free energy calculations

The role of the primary binding residue (P1) in complexes between three different subtilases (subtilisin Carlsberg, thermitase and proteinase K) and their canonical protein inhibitor eglin c have been studied by free energy calculations. Based on the crystal structures of eglin c in complex with subtilisin Carlsberg and thermitase, and a homology model of the eglin c-proteinase K complex, a total of 57 mutants have been constructed and docked into their host proteins. The binding free energy was then calculated using molecular dynamics (MD) simulations combined with the linear interaction energy (LIE) method for all complexes differing only in the nature of the amino acid at the P1 position. LIE calculations for 19 different complexes for each subtilase were thus carried out excluding proline. The effects of substitutions at the P1 position on the binding free energies are found to be very large, and positively charged residues (Arg, Lys and His) are particularly deleterious for all three enzymes. The charged variants of the acidic side chains are found to bind more favorably as compared to their protonated states in all three subtilases. Furthermore, hydrophobic amino acids are accommodated most favorably at the S1-site in all three enzymes. Comparison of the three series of binding free energies shows only minor differences in the 19 computed relative binding free energies among these subtilases. This is further reflected in the correlation coefficient between the 23 relative binding free energies obtained, including the possible protonation states of ionizable side chains, but excluding the P1 Pro, for subtilisin Carlsberg versus thermitase (0.95), subtilisin versus proteinase K (0.94) and thermitase versus proteinase K (0.96).

Calculations of solute and solvent entropies from molecular dynamics simulations

The translational, rotational and conformational (vibrational) entropy contributions to ligand-receptor binding free energies are analyzed within the standard formulation of statistical thermodynamics. It is shown that the partitioning of the binding entropy into different components is to some extent arbitrary, but an appropriate method to calculate both translational and rotational entropy contributions to noncovalent association is by estimating the configurational volumes of the ligand in the bound and free states. Different approaches to calculating solute entropies using free energy perturbation calculations, configurational volumes based on root-mean-square fluctuations and covariance matrix based quasiharmonic analysis are illustrated for some simple molecular systems. Numerical examples for the different contributions demonstrate that theoretically derived results are well reproduced by the approximations. Calculation of solvent entropies, either using total potential energy averages or van't Hoff plots, are carried out for the case of ion solvation in water. Although convergence problems will persist for large and complex simulation systems, good agreement with experiment is obtained here for relative and absolute ion hydration entropies. We also outline how solvent and solute entropic contributions are taken into account in empirical binding free energy calculations using the linear interaction energy method. In particular it is shown that empirical scaling of the nonpolar intermolecular ligand interaction energy effectively takes into account size dependent contributions to the binding free energy.

Modeling electrostatic effects in proteins

Electrostatic energies provide what is perhaps the most effective tool for structure-function correlation of biological molecules. This review considers the current state of simulations of electrostatic energies in macromolecules as well as the early developments of this field. We focus on the relationship between microscopic and macroscopic models, considering the convergence problems of the microscopic models and the fact that the dielectric 'constants' in semimacroscopic models depend on the definition and the specific treatment. The advances and the challenges in the field are illustrated considering a wide range of functional properties including pK(a)'s, redox potentials, ion and proton channels, enzyme catalysis, ligand binding and protein stability. We conclude by pointing out that, despite the current problems and the significant misunderstandings in the field, there is an overall progress that should lead eventually to quantitative descriptions of electrostatic effects in proteins and thus to quantitative descriptions of the function of proteins.

Crystallization and preliminary crystallographic studies of human septin 1 with site-directed mutations

Septin 1 is a member of an evolutionarily conserved family of GTP-binding and filament-forming proteins named septins, which function in diverse processes including cytokinasis, vesicle trafficking, apoptosis, remodelling of the cytoskeleton, infection, neurodegeneration and neoplasia. Human septin 1 has been expressed and purified, but suffers from severe aggregation. Studies have shown that septin 1 with site-directed mutations of five serine residues (Ser19, Ser206, Ser307, Ser312 and Ser315) has a much lower degree of aggregation and better structural homogeneity and that the mutations cause only slight perturbations in the secondary structure of septin 1. This septin 1 mutant was crystallized and diffraction data were collected to 2.5 A resolution. The space group is P422, with unit-cell parameters a = b = 106.028, c = 137.852 A.