Probing the Binding Affinity by Jarzynski's Nonequilibrium Binding Free Energy and Rupture Time

Examining sialic acid derivatives as potential inhibitors of SARS-CoV-2 spike protein receptor binding domain

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has been the primary reason behind the COVID-19 global pandemic which has affected millions of lives worldwide. The fundamental cause of the infection is the molecular binding of the viral spike protein receptor binding domain (SP-RBD) with the human cell angiotensin-converting enzyme 2 (ACE2) receptor. The infection can be prevented if the binding of RBD-ACE2 is resisted by utilizing certain inhibitors or drugs that demonstrate strong binding affinity towards the SP RBD. Sialic acid based glycans found widely in human cells and tissues have notable propensity of binding to viral proteins of the coronaviridae family. Recent experimental literature have used N-acetyl neuraminic acid (Sialic acid) to create diagnostic sensors for SARS-CoV-2, but a detailed interrogation of the underlying molecular mechanisms is warranted. Here, we perform all atom molecular dynamics (MD) simulations for the complexes of certain Sialic acid-based molecules with that of SP RBD of SARS CoV-2. Our results indicate that Sialic acid not only reproduces a binding affinity comparable to the RBD-ACE2 interactions, it also assumes the longest time to dissociate completely from the protein binding pocket of SP RBD. Our predictions corroborate that a combination of electrostatic and van der Waals energies as well the polar hydrogen bond interactions between the RBD residues and the inhibitors influence free energy of binding.Communicated by Ramaswamy H. Sarma.

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.

DeltaGzip: Computing Biopolymer-Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression

The design of biosequences for biosensing and therapeutics is a challenging multistep search and optimization task. In principle, computational modeling may speed up the design process by virtual screening of sequences based on their binding affinities to target molecules. However, in practice, existing machine-learned models trained to predict binding affinities lack the flexibility with respect to reaction conditions, and molecular dynamics simulations that can incorporate reaction conditions suffer from high computational costs. Here, we describe a computational approach called DeltaGzip that evaluates the free energy of binding in biopolymer-ligand complexes from ultrashort equilibrium molecular dynamics simulations. The entropy of binding is evaluated using the Kolmogorov complexity definition of entropy and approximated using a lossless compression algorithm, Gzip. We benchmark the method on a well-studied data set of protein-ligand complexes comparing the predictions of DeltaGzip to the free energies of binding obtained using Jarzynski equality and experimental measurements.

Imidazole[1,5-a]pyridine derivatives as EGFR tyrosine kinase inhibitors unraveled by umbrella sampling and steered molecular dynamics simulations

Although the use of the tyrosine kinase inhibitors (TKIs) has been proved that it can save live in a cancer treatment, the currently used drugs bring in many undesirable side-effects. Therefore, the search for new drugs and an evaluation of their efficiency are intensively carried out. Recently, a series of eighteen imidazole[1,5-a]pyridine derivatives were synthetized by us, and preliminary analyses pointed out their potential to be an important platform for pharmaceutical development owing to their promising actions as anticancer agents and enzyme (kinase, HIV-protease,…) inhibitors. In the present theoretical study, we further analyzed their efficiency in using a realistic scenario of computational drug design. Our protocol has been developed to not only observe the atomistic interaction between the EGFR protein and our 18 novel compounds using both umbrella sampling and steered molecular dynamics simulations, but also determine their absolute binding free energies. Calculated properties of the 18 novel compounds were in detail compared with those of two known drugs, erlotinib and osimertinib, currently used in cancer treatment. Inspiringly the simulation results promote three imidazole[1,5-a]pyridine derivatives as promising inhibitors into a further step of clinical trials.

The Thermodynamic Properties of Fat10ylated Proteins Are Regulated by the Fat10ylation Site

Degradation of proteins by the proteasome is crucial in regulating their levels in the cell. Post-translational modifications, such as ubiquitylation and Fat10ylation, trigger proteasomal degradation of the substrate proteins. While ubiquitylation regulates multiple cellular pathways, Fat10ylation functions explicitly in the inflammatory response pathway. At the proteasome, ubiquitin is recycled after being cleaved from the substrate, while Fat10 is degraded simultaneously with its substrate. Although the thermodynamic properties of the substrate are critical for effective proteasomal degradation, they remain poorly understood for the Fat10-proteasome pathway. We studied the thermodynamic properties of the Fat10∼substrate conjugate to uncover mechanistic details of the pathway. First, the mechanical unfolding of Fat10∼substrate was studied by molecular dynamics simulations, which suggested that the unfolding pathway and unfolding energy of the substrate depend on the site of Fat10 modification. We also investigated different pathways for the entry of the Fat10∼substrate into the proteasome core. Our analysis supports a model where the entry of Fat10, followed by the substrate, is the energetically preferred pathway. Further, we studied Fat10's effect on the thermodynamic properties of distinct substrates, considering their size, flexibility, and surface properties. The results uncovered significant entropic destabilization of substrates due to Fat10ylation, particularly in smaller substrates. For larger substrates, multi-monoFat10ylation is necessary to induce destabilization. Our study further reveals that Fat10 modification at negative patches on substrate surfaces is essential for optimal destabilization and subsequent degradation. These findings provide atomistic insights into the degradation mechanisms in the Fat10 proteasome pathway with potential implications for therapeutic interventions.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Dissociation Rate Calculation via Constant-Force Steered Molecular Dynamics Simulation

Steered molecular dynamics (SMD) simulations are used to study molecular dissociation events by applying a harmonic force to the molecules and pulling them at a constant velocity. Instead of constant-velocity pulling, we use a constant force: the constant-force SMD (CF-SMD) simulation. The CF-SMD simulation employs a constant force to reduce the activation barrier of molecular dissociation, thereby enhancing the dissociation event. Here, we present the capability of the CF-SMD simulation to estimate the dissociation time at equilibrium. We performed all-atom CF-SMD simulations for NaCl and protein-ligand systems, producing dissociation time at various forces. We extrapolated these values to the dissociation rate without a constant force using Bell's model or the Dudko-Hummer-Szabo model. We demonstrate that the CF-SMD simulations with the models predicted the dissociation time in equilibrium. A CF-SMD simulation is a powerful tool for estimating the dissociation rate in a direct and computationally efficient manner.

Hepatoprotective Effect of Millettia dielsiana: In Vitro and In Silico Study

In silico docking studies of 50 selected compounds from Millettia dielsiana Harms ex Diels (family Leguminosae) were docked into the binding pocket of the PI3K/mTOR protein. In there, compounds trans-3-O-p-hydroxycinnamoyl ursolic acid (1) and 5,7,4'-trihydroxyisoflavone 7-O-β-D-apiofuranosyl-(1→6)-β-D-glucopyranoside (2) are predicted to be very promising inhibitors against PI3K/mTOR. They direct their cytotoxic activity against Hepatocellular carcinoma with binding affinity (BA) values, the pulling work spent to the co-crystallized ligand from the binding site of PI3K/mTOR (W and F_max), and the non-equilibrium binding free energy (∆G_neq^Jar) as BA values = -9.237 and -9.083 kcal/mol, W = 83.5 ± 10.6 kcal/mol with F_max = 336.2 ± 45.3 pN and 126.6 ± 21.7 kcal/mol with F_max = 430.3 ± 84.0 pN, and ∆G_neq^Jar = -69.86074 and -101.2317 kcal/mol, respectively. In molecular dynamic simulation, the RMSD value of the PI3K/mTOR complex with compounds (1 and 2) was in the range of 0.3 nm to the end of the simulation. Therefore, the compounds (1 and 2) are predicted to be very promising inhibitors against PI3K/mTOR. The crude extract, ethyl acetate fraction and compounds (1 and 2) from Millettia dielsiana exhibited moderate to potent in vitro cytotoxicity on Hepatocellular carcinoma cell line with IC₅₀ values of 81.2 µg/mL, 60.4 µg/mL, 23.1 μM, and 16.3 μM, respectively, and showed relatively potent to potent in vitro antioxidant activity on mouse hepatocytes with ED₅₀ values of 24.4 µg/mL, 19.3 µg/mL, 30.7 μM, and 20.5 μM, respectively. In conclusion, Millettia dielsiana and compounds (1 and 2) are predicted to have very promising cytotoxic activity against Hepatocellular carcinoma and have a hepatoprotective effect.

Free energy and kinetic rate calculation via non-equilibrium molecular simulation: application to biomolecules

Non-equilibrium molecular dynamics (NEMD) simulation has been recognized as a powerful tool for examining biomolecules and provides fruitful insights into not only non-equilibrium but also equilibrium processes. We review recent advances in NEMD simulation and relevant, fundamental results of non-equilibrium statistical mechanics. We first introduce Crooks fluctuation theorem and Jarzynski equality that relate free energy difference to work done on a physical system during a non-equilibrium process. The theorems are beneficial for the analysis of NEMD trajectories. We then describe rate theory, a framework to calculate molecular kinetics from a non-equilibrium process; this theoretical framework enables us to calculate a reaction time-mean-first passage time-from NEMD trajectories. We, in turn, present recent NEMD techniques that apply an external force to a system to enhance molecular dissociation and introduce their application to biomolecules. Lastly, we show the current status of an appropriate selection of reaction coordinates for NEMD simulation.

Antibody-nanobody combination increases their neutralizing activity against SARS-CoV-2 and nanobody H11-H4 is effective against Alpha, Kappa and Delta variants

The global spread of COVID-19 is devastating health systems and economies worldwide. While the use of vaccines has yielded encouraging results, the emergence of new variants of SARS-CoV-2 shows that combating COVID-19 remains a big challenge. One of the most promising treatments is the use of not only antibodies, but also nanobodies. Recent experimental studies revealed that the combination of antibody and nanobody can significantly improve their neutralizing ability through binding to the SARS-CoV-2 spike protein, but the molecular mechanisms underlying this observation remain largely unknown. In this work, we investigated the binding affinity of the CR3022 antibody and H11-H4 nanobody to the SARS-CoV-2 receptor binding domain (RBD) using molecular modeling. Both all-atom steered molecular dynamics simulations and coarse-grained umbrella sampling showed that, consistent with the experiment, CR3022 associates with RBD more strongly than H11-H4. We predict that the combination of CR3022 and H11-H4 considerably increases their binding affinity to the spike protein. The electrostatic interaction was found to control the association strength of CR3022, but the van der Waals interaction dominates in the case of H11-H4. However, our study for a larger set of nanobodies and antibodies showed that the relative role of these interactions depends on the specific complex. Importantly, we showed Beta, Gamma, Lambda, and Mu variants reduce the H11-H4 activity while Alpha, Kappa and Delta variants increase its neutralizing ability, which is in line with experiment reporting that the nanobody elicited from the llama is very promising for fighting against the Delta variant.

Improving the Potential of Mean Force and Nonequilibrium Pulling Simulations by Simultaneous Alchemical Modifications

We present an approach combining alchemical modifications and physical-pathway methods to calculate absolute binding free energies. The employed physical-pathway method is either a stratified umbrella sampling to calculate a potential of mean force or nonequilibrium pulling. We devised two basic approaches: the simultaneous approach (S-approach), where, along the physical unbinding pathway, an alchemical transformation of ligand-protein interactions is installed and deinstalled, and the prior-plus-simultaneous approach (PPS-approach), where, prior to the physical-pathway simulation, an alchemical transformation of ligand-protein interactions is installed in the binding site and deinstalled during the physical-pathway simulation. Using a mutant of T4 lysozyme with a benzene ligand as an example, we show that installation and deinstallation of soft-core interactions concurrent with physical ligand unbinding (S-approach) allow successful potential of mean force calculations and nonequilibrium pulling simulations despite the problems posed by the occluded nature of the lysozyme binding pocket. Good agreement between the potential of the mean-force-based S-approach and double decoupling simulations as well as a remarkable efficiency and accuracy of the nonequilibrium-pulling-based S-approach is found. The latter turned out to be more compute-efficient than the potential of mean force calculation by approximately 70%. Furthermore, we illustrate the merits of reducing ligand-protein interactions prior to potential of mean force calculations using the murine double minute homologue protein MDM2 with a p53-derived peptide ligand (PPS-approach). Here, the problem of breaking strong interactions in the binding pocket is transferred to a prior alchemical transformation that reduces the free-energy barrier between the bound and unbound state in the potential of mean force. Besides, disentangling physical ligand displacement from the deinstallation of ligand-protein interactions was seen to allow a more uniform sampling of distance histograms in the umbrella sampling. In the future, physical ligand unbinding combined with simultaneous alchemical modifications may prove useful in the calculation of protein-protein binding free energies, where sampling problems posed by multiple, possibly sticky interactions and potential steric clashes can thus be reduced.

Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution

Steered molecular dynamics (SMD) simulation is a powerful method in computer-aided drug design as it can be used to access the relative binding affinity with high precision but with low computational cost. The success of SMD depends on the choice of the direction along which the ligand is pulled from the receptor-binding site. In most simulations, the unidirectional pathway was used, but in some cases, this choice resulted in the ligand colliding with the complex surface of the exit tunnel. To overcome this difficulty, several variants of SMD with multidirectional pulling have been proposed, but they are not completely devoid of disadvantages. Here, we have proposed to determine the direction of pulling with a simple scoring function that minimizes the receptor–ligand interaction, and an optimization algorithm called differential evolution is used for energy minimization. The effectiveness of our protocol was demonstrated by finding expulsion pathways of Huperzine A and camphor from the binding site of Torpedo California acetylcholinesterase and P450cam proteins, respectively, and comparing them with the previous results obtained using memetic sampling and random acceleration molecular dynamics. In addition, by applying this protocol to a set of ligands bound with LSD1 (lysine specific demethylase 1), we obtained a much higher correlation between the work of pulling force and experimental data on the inhibition constant IC50 compared to that obtained using the unidirectional approach based on minimal steric hindrance.

Cocktail of REGN Antibodies Binds More Strongly to SARS-CoV-2 Than Its Components, but the Omicron Variant Reduces Its Neutralizing Ability

A promising approach to combat Covid-19 infections is the development of effective antiviral antibodies that target the SARS-CoV-2 spike protein. Understanding the structures and molecular mechanisms underlying the binding of antibodies to SARS-CoV-2 can contribute to quickly achieving this goal. Recently, a cocktail of REGN10987 and REGN10933 antibodies was shown to be an excellent candidate for the treatment of Covid-19. Here, using all-atom steered molecular dynamics and coarse-grained umbrella sampling, we examine the interactions of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with REGN10987 and REGN10933 separately as well as together. Both computational methods show that REGN10933 binds to RBD more strongly than REGN10987. Importantly, the cocktail binds to RBD (simultaneous binding) more strongly than its components. The dissociation constants of REGN10987-RBD and REGN10933-RBD complexes calculated from the coarse-grained simulations are in good agreement with the experimental data. Thus, REGN10933 is probably a better candidate for treating Covid-19 than REGN10987, although the cocktail appears to neutralize the virus more efficiently than REGN10933 or REGN10987 alone. The association of REGN10987 with RBD is driven by van der Waals interactions, while electrostatic interactions dominate in the case of REGN10933 and the cocktail. We also studied the effectiveness of these antibodies on the two most dangerous variants Delta and Omicron. Consistent with recent experimental reports, our results confirmed that the Omicron variant reduces the neutralizing activity of REGN10933, REGN10987, and REGN10933+REGN10987 with the K417N, N440K, L484A, and Q498R mutations playing a decisive role, while the Delta variant slightly changes their activity.

Dependence of Work on the Pulling Speed in Mechanical Ligand Unbinding

In single-molecule force spectroscopy, the rupture force F_max required for mechanical unfolding of a biomolecule or for pulling a ligand out of a binding site depends on the pulling speed V and, in the linear Bell-Evans regime, F_max ∼ ln(V). Recently, it has been found that non-equilibrium work W is better than F_max in describing relative ligand binding affinity, but the dependence of W on V remains unknown. In this paper, we developed an analytical theory showing that in the linear regime, W ∼ c₁ ln(V) + c₂ ln²(V), where c₁ and c₂ are constants. This quadratic dependence was also confirmed by all-atom steered molecular dynamics simulations of protein-ligand complexes. Although our theory was developed for ligand unbinding, it is also applicable to other processes, such as mechanical unfolding of proteins and other biomolecules, due to its universality.

Electrostatic Interactions Explain the Higher Binding Affinity of the CR3022 Antibody for SARS-CoV-2 than the 4A8 Antibody

A structural understanding of the mechanism by which antibodies bind SARS-CoV-2 at the atomic level is highly desirable as it can tell the development of more effective antibodies to treat Covid-19. Here, we use steered molecular dynamics (SMD) and coarse-grained simulations to estimate the binding affinity of the monoclonal antibodies CR3022 and 4A8 to the SARS-CoV-2 receptor-binding domain (RBD) and SARS-CoV-2 N-terminal domain (NTD). Consistent with experiments, our SMD and coarse-grained simulations both indicate that CR3022 has a higher affinity for SARS-CoV-2 RBD than 4A8 for the NTD, and the coarse-grained simulations indicate the former binds three times stronger to its respective epitope. This finding shows that CR3022 is a candidate for Covid-19 therapy and is likely a better choice than 4A8. Energetic decomposition of the interaction energies between these two complexes reveals that electrostatic interactions explain the difference in the observed binding affinity between the two complexes. This result could lead to a new approach for developing anti-Covid-19 antibodies in which good candidates must contain charged amino acids in the area of contact with the virus.

Remdesivir Strongly Binds to Both RNA-Dependent RNA Polymerase and Main Protease of SARS-CoV-2: Evidence from Molecular Simulations

The outbreak of a new coronavirus SARS-CoV-2 (severe acute respiratory syndrome-coronavirus 2) has caused a global COVID-19 (coronavirus disease 2019) pandemic, resulting in millions of infections and thousands of deaths around the world. There is currently no drug or vaccine for COVID-19, but it has been revealed that some commercially available drugs are promising, at least for treating symptoms. Among them, remdesivir, which can block the activity of RNA-dependent RNA polymerase (RdRp) in old SARS-CoV and MERS-CoV viruses, has been prescribed to COVID-19 patients in many countries. A recent experiment showed that remdesivir binds to SARS-CoV-2 with an inhibition constant of μM, but the exact target has not been reported. In this work, combining molecular docking, steered molecular dynamics, and umbrella sampling, we examined its binding affinity to two targets including the main protease (Mpro), also known as 3C-like protease, and RdRp. We showed that remdesivir binds to Mpro slightly weaker than to RdRp, and the corresponding inhibition constants, consistent with the experiment, fall to the μM range. The binding mechanisms of remdesivir to two targets differ in that the electrostatic interaction is the main force in stabilizing the RdRp-remdesivir complex, while the van der Waals interaction dominates in the Mpro-remdesivir case. Our result indicates that remdesivir can target not only RdRp but also Mpro, which can be invoked to explain why this drug is effective in treating COVID-19. We have identified residues of the target protein that make the most important contribution to binding affinity, and this information is useful for drug development for this disease.

Does SARS-CoV-2 Bind to Human ACE2 More Strongly Than Does SARS-CoV?

The 2019 novel coronavirus (SARS-CoV-2) epidemic, which was first reported in December 2019 in Wuhan, China, was declared a pandemic by the World Health Organization in March 2020. Genetically, SARS-CoV-2 is closely related to SARS-CoV, which caused a global epidemic with 8096 confirmed cases in more than 25 countries from 2002 to 2003. Given the significant morbidity and mortality rate, the current pandemic poses a danger to all of humanity, prompting us to understand the activity of SARS-CoV-2 at the atomic level. Experimental studies have revealed that spike proteins of both SARS-CoV-2 and SARS-CoV bind to angiotensin-converting enzyme 2 (ACE2) before entering the cell for replication. However, the binding affinities reported by different groups seem to contradict each other. Wrapp et al. (Science2020, 367, 1260–1263) showed that the spike protein of SARS-CoV-2 binds to the ACE2 peptidase domain (ACE2-PD) more strongly than does SARS-CoV, and this fact may be associated with a greater severity of the new virus. However, Walls et al. (Cell2020, 181, 281–292) reported that SARS-CoV-2 exhibits a higher binding affinity, but the difference between the two variants is relatively small. To understand the binding mechnism and experimental results, we investigated how the receptor binding domain (RBD) of SARS-CoV (SARS-CoV-RBD) and SARS-CoV-2 (SARS-CoV-2-RBD) interacts with a human ACE2-PD using molecular modeling. We applied a coarse-grained model to calculate the dissociation constant and found that SARS-CoV-2 displays a 2-fold higher binding affinity. Using steered all-atom molecular dynamics simulations, we demonstrate that, like a coarse-grained simulation, SARS-CoV-2-RBD was associated with ACE2-PD more strongly than was SARS-CoV-RBD, as evidenced by a higher rupture force and larger pulling work. We show that the binding affinity of both viruses to ACE2 is driven by electrostatic interactions.

How Good is Jarzynski's Equality for Computer-Aided Drug Design?

Accurate determination of the binding affinity of the ligand to the receptor remains a difficult problem in computer-aided drug design. Here, we study and compare the efficiency of Jarzynski’s equality (JE) combined with steered molecular dynamics and the linear interaction energy (LIE) method by assessing the binding affinity of 23 small compounds to six receptors, including β-lactamase, thrombin, factor Xa, HIV-1 protease (HIV), myeloid cell leukemia-1, and cyclin-dependent kinase 2 proteins. It was shown that Jarzynski’s nonequilibrium binding free energy ΔG_neq^Jar correlates with the available experimental data with the correlation levels R = 0.89, 0.86, 0.83, 0.80, 0.83, and 0.81 for six data sets, while for the binding free energy ΔG_LIE obtained by the LIE method, we have R = 0.73, 0.80, 0.42, 0.23, 0.85, and 0.01. Therefore, JE is recommended to be used for ranking binding affinities as it provides accurate and robust results. In contrast, LIE is not as reliable as JE, and it should be used with caution, especially when it comes to new systems.

ModiBodies: A computational method for modifying nanobodies in nanobody-antigen complexes to improve binding affinity and specificity

Nanobodies are special derivatives of antibodies, which consist of single domain fragments. They have become of considerable interest as next-generation biotechnological tools for antigen recognition. They can be easily engineered due to their high stability and compact size. Nanobodies have three complementarity-determining regions, CDRs, which are enlarged to provide a similar binding surface to that of human immunoglobulins. Here, we propose a benchmark testing algorithm that uses 3D structures of already existing protein-nanobody complexes as initial structures followed by successive mutations on the CDR domains. The aim is to find optimum binding amino acids for hypervariable residues of CDRs. We use molecular dynamics simulations to compare the binding energies of the resulting complexes with that of the known complex and accept those that are improved by mutations. We use the MDM4-VH9 complex, (PDB id 2VYR), fructose-bisphosphate aldolase from Trypanosoma congolense (PDB id 5O0W) and human lysozyme (PDB id 4I0C) as benchmark complexes. By using this algorithm, better binding nanobodies can be generated in a short amount of time. We suggest that this method can complement existing immune and synthetic library-based methods, without a need for extensive experimentation or large libraries.

Investigation of Binding Affinity between Potential Antiviral Agents and PB2 Protein of Influenza A: Non-equilibrium Molecular Dynamics Simulation Approach

The PB2 protein of the influenza virus RNA polymerase is a major virulence determinant of influenza viruses. It binds to the cap structure at the 5' end of host mRNA to generate short capped RNA fragments that are used as primers for viral transcription named cap-snatching. A large number of the compounds were shown to bind the minimal cap-binding domain of PB2 to inhibit the cap-snatching machinery. However, their binding in the context of an extended form of the PB2 protein has remained elusive. A previous study reported some promising compounds including azaindole and hydroxymethyl azaindole, which were analyzed here to predict binding affinity to PB2 protein using the steered molecular dynamics (SMD) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods. The results show that the rupture force (F_max) value of three complexes is in agreement with the binding free energy value (ΔG_bind) estimated by the MM-PBSA method, whereas for the non-equilibrium pulling work (W_pull) value a small difference between A_PB2-4 and A_PB2-12 was observed. The binding affinity results indicate the A_PB2-12 complex is more favorable than the A_PB2-4 and A_PB2-16 complexes, which means the inhibitor (12) has the potential to be further developed as anti-influenza agents in the treatment of influenza A.

Detailed potential of mean force studies on host-guest systems from the SAMPL6 challenge

Accurately predicting receptor-ligand binding free energies is one of the holy grails of computational chemistry with many applications in chemistry and biology. Many successes have been reported, but issues relating to sampling and force field accuracy remain significant issues affecting our ability to reliably calculate binding free energies. In order to explore these issues in more detail we have examined a series of small host-guest complexes from the SAMPL6 blind challenge, namely octa-acids (OAs)-guest complexes and Curcurbit[8]uril (CB8)-guest complexes. Specifically, potential of mean force studies using umbrella sampling combined with the weighted histogram method were carried out on both systems with both known and unknown binding affinities. We find that using standard force fields and straightforward simulation protocols we are able to obtain satisfactory results, but that simply scaling our results allows us to significantly improve our predictive ability for the unknown test sets: the overall RMSD of the binding free energy versus experiment is reduced from 5.59 to 2.36 kcal/mol; for the CB8 test system, the RMSD goes from 8.04 to 3.51 kcal/mol, while for the OAs test system, the RSMD goes from 2.89 to 0.95 kcal/mol. The scaling approach was inspired by studies on structurally related known benchmark sets: by simply scaling, the RMSD was reduced from 6.23 to 1.19 kcal/mol and from 2.96 to 0.62 kcal/mol for the CB8 benchmark system and the OA benchmark system, respectively. We find this scaling procedure to correct absolute binding affinities to be highly effective especially when working across a "congeneric" series with similar charge states. It is less successful when applied to mixed ligands with varied charges and chemical characteristics, but improvement is still realized in the present case. This approach suggests that there are large systematic errors in absolute binding free energy calculations that can be straightforwardly accounted for using a scaling procedure. Random errors are still an issue, but near chemical accuracy can be obtained using the present strategy in select cases.