Side-chain Modeling Research Articles

Solvent accessible surface area-assessed molecular basis of osmolyte-induced protein stability.

In solvent-modulated protein folding, under certain physiological conditions, an equilibrium exists between the unfolded and folded states of the protein without any need to break or make a covalent bond. In this process, interactions between various protein groups (peptides) and solvent molecules are known to play a major role in determining the directionality of the chemical reaction. However, an understanding of the mechanism of action of the co(solvent) by a generic theoretical underpinning is lacking. In this study, a generic solvation model is developed based on statistical mechanics and the thermodynamic transfer free energy model by considering the microenvironment polarity of the interacting co(solvent)-protein system. According to this model, polarity and the fractional solvent-accessible surface areas contribute to the interaction energies. The present model includes various orientations of participating interactant solvent surfaces of suitable areas. As model systems, besides the backbone we consider naturally occurring amino acid residues solvated in ten different osmolytes, small organic compounds known to modulate protein stability. The present model is able to predict the correct trend of the osmolyte-peptide interactions ranging from stabilizing to destabilizing not only for the backbone but also for side chains. Our model predicts Asn, Gln, Asp, Glu, Arg and Pro to be highly stable in most of the protecting osmolytes while Ala, Val, Ile, Leu, Thr, Met, Lys, Phe, Trp and Tyr are predicted to be moderately stable, and Ser, Cys and Histidine are predicted to be least stable. However, in denaturing solvents, both backbone and side chain models show similar stabilities in urea and guanidine. One of the important aspects of this model is that it is essentially parameter-free and consistent with the electrostatics of the interaction partners that make this model suitable for estimating any solute-solvent interaction energies.

Resource analysis of quantum algorithms for coarse-grained protein folding models

Protein folding processes are a vital aspect of molecular biology that is hard to simulate with conventional computers. Quantum algorithms have been proven superior for certain problems and may help tackle this complex life science challenge. We analyze the resource requirements for simulating simplified yet computationally challenging protein folding models on a quantum computer, assessing the feasibility of these existing approaches in the current and near-future technological landscape. We calculate the minimum number of qubits, interactions, and two-qubit gates necessary to build a heuristic quantum algorithm with the specific information of a folding problem. Particularly, we focus on the resources needed to build quantum operations based on the Hamiltonian linked to the protein folding models for a given amino acid count. Such operations are a fundamental component of these quantum algorithms, guiding the evolution of the quantum state for efficient computations. Specifically, we study coarse-grained folding models on the lattice and the fixed backbone side-chain conformation model and assess their compatibility with the constraints of existing quantum hardware given different bit encodings. We conclude that the number of qubits required falls within current technological capabilities. However, the limiting factor is the high number of interactions in the Hamiltonian, resulting in a quantum gate count unavailable today. Published by the American Physical Society 2024

OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer

Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.

Secure cross-chain interaction solution in multi-blockchain environment

In the context of the increasingly diversified blockchain technology, interoperability among heterogeneous blockchains has become key to further advancing this field. Existing cross-chain technologies, while facilitating data and asset exchange between different blockchains to some extent, have exposed issues such as insufficient security, low efficiency, and inconsistent standards. Consequently, these issues give rise to significant obstacles in terms of both scalability and seamless communication among blockchains within a multi-chain framework. To address this, this paper proposes an efficient method for cross-chain interaction in a multi-chain environment. Building upon the traditional sidechain model, this method employs smart contracts and hash time-locked contracts (HTLCs) to design a cross-chain interaction scheme. This approach decentralizes the execution of locking, verifying, and unlocking stages in cross-chain transactions, effectively avoiding centralization risks associated with third-party entities in the process. It also greatly enhances the efficiency of fund transfers between the main chain and sidechains, while ensuring the security of cross-chain transactions to some extent. Additionally, this paper innovatively proposes a cross-chain data interaction strategy. Through smart contracts on the main chain, data from sidechains can be uploaded, verified, and stored on the main chain, achieving convenient and efficient cross-chain data sharing. The contribution of this paper is the development of a decentralized protocol that coordinates the execution of cross-chain interactions without the need to trust external parties, thereby reducing the risk of centralization and enhancing security. Experimental results validate the effectiveness of our solution in increasing transaction security and efficiency, with significant improvements over existing models. Our experiments emphasize the system's ability to handle a variety of transaction scenarios with improved throughput and reduced latency, highlighting the practical applicability and scalability of our approach.

Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling.

Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.

OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow.

For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.

OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling.

Predicting the effect of protein mutation is crucial in many applications such as protein design, protein evolution, and genetic disease analysis. Structurally, mutation is basically the replacement of the side chain of a particular residue. Therefore, accurate side-chain modeling is useful in studying the effect of mutation. Here, we propose a computational method, namely, OPUS-Mut, which significantly outperforms other backbone-dependent side-chain modeling methods including our previous method OPUS-Rota4. We evaluate OPUS-Mut by four case studies on Myoglobin, p53, HIV-1 protease, and T4 lysozyme. The results show that the predicted structures of side chains of different mutants are consistent well with their experimentally determined results. In addition, when the residues with significant structural shifts upon the mutation are considered, it is found that the extent of the predicted structural shift of these affected residues can be correlated reasonably well with the functional changes of the mutant measured by experiments. OPUS-Mut can also help one to identify the harmful and benign mutations and thus may guide the construction of a protein with relatively low sequence homology but with a similar structure.

Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1.

With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L1043.32 and L2556.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.

GeoPacker: A novel deep learning framework for protein side-chain modeling.

Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in protein sequence design. However, highly efficient and accurate protein side-chain prediction methods that can give detailed atomic interactions are still lacking. In the present study, we developed a deep learning based method, GeoPacker, that uses geometric deep learning coupled ResNet for protein side-chain modeling. GeoPacker explicitly represents atomic interactions with rotational and translational invariance for information extraction of relative locations. GeoPacker outperformed the state-of-the-art energy function-based methods in side-chain structure prediction accuracy and runs about 10 and 700 times faster than the deep learning-based method DLPacker and OPUS-rota4 with comparable prediction accuracy, respectively. The performance of GeoPacker does not depend on the secondary structures that the residues belong to. GeoPacker gives highly accurate predictions for buried residues in the protein core as well as protein-protein interface, making it a useful tool for protein structure modeling, protein, and interaction design.

Kinetics of Reactions of Dirhodium and Diruthenium Paddlewheel Tetraacetate Complexes with Nucleophilic Protein Sites: Computational Insights.

Recently, dirhodium and diruthenium paddlewheel complexes have drawn attention as perspective anticancer drugs. In this study, the kinetics of reaction of typical paddlewheel scaffolds Rh2(μ-O2CCH3)4(H2O)2, Ru2(μ-O2CCH3)4(H2O)Cl, and [Ru2(μ-O2CCH3)4(HO)Cl]- with protein nucleophiles were investigated by means of the density functional theory. The substitution of axial ligands─water and chloride─by the models of protein residue side chains was analyzed, revealing the binding selectivity displayed by these paddlewheel metal scaffolds. The substitution of water is under a thermodynamic control, in which, although the Arg, Cys-, and Sec- residues are the most favorable, their binding is expected to be scarcely selective in a biological context. On the other hand, the replacement of the axial water with a more stable hydroxo ligand induces the chloride substitution in diRu complexes, which also targets Arg, Cys-, and Sec-, although with a moderately higher activation barrier for any examined protein residue. Additionally, the carried out characterization of the geometrical parameters of the transition states permitted determination of the impact of an increased steric hindrance of diRh and diRu complexes on their protein site selectivity. This study corroborates the idea of the substitution of the acetate ligands with biologically active, but more hindering, carboxylate ligands, in order to yield dual acting metallodrugs. This study allows us to assume that the delivery of diRu paddlewheel complexes in their monoanionic form [Ru2(μ-O2CR)4(OH)Cl]- decorated by the bulky substituents R may constitute an approach to augment the selectivity toward anticancer targets, such as TrxR in tumor cells, although under the condition that such a selectivity is operative only in high pH conditions.

Studying protein-protein interaction through side-chain modeling method OPUS-Mut.

Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

Comparative evaluation of spin-label modeling methods for protein structural studies

Site-directed spin-labeling electron paramagnetic resonance spectroscopy is a powerful technique for the investigation of protein structure and dynamics. Accurate spin-label modeling methods are essential to make full quantitative use of site-directed spin-labeling electron paramagnetic resonance data for protein modeling and model validation. Using a set of double electron-electron resonance data from seven different site pairs on maltodextrin/maltose-binding protein under two different conditions using five different spin labels, we compare the ability of two widely used spin-label modeling methods, based on accessible volume sampling and rotamer libraries, to predict experimental distance distributions. We present a spin-label modeling approach inspired by canonical side-chain modeling methods and compare modeling accuracy with the established methods.

Simultaneous prediction of antibody backbone and side-chain conformations with deep learning.

Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side-chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side-chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering it particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of the model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side-chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side-chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side-chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.

N-Acetyl Side-Chain Conformation in Saccharides: Solution Models Obtained from MA'AT Analysis.

MA'AT analysis has been applied to model the conformational properties of N-acetyl side-chains in biologically important GlcNAc and ManNAc monosaccharides and in a βGlcNAc-(1→4)-βGlcNAc disaccharide. Density functional theory calculations were conducted to obtain parameterized equations that relate the magnitudes and signs of 10 spin-coupling constants to conformations of the C2-N2 bonds of GlcNAc and ManNAc. Six of these equations were used with experimental J-couplings, measured in H2O/2H2O and DMSO-d6 solvents in selectively 13C-labeled compounds, to model the C1-C2-N2-C1' torsion angle (θ1) in GlcNAc and ManNAc residues. MA'AT analysis gave mean values of θ1 of 106° for αGlcNAc and ∼116° for βGlcNAc residues, with circular standard deviations (CSDs) of 21-22° in aqueous solution, in excellent agreement with those obtained by aqueous molecular dynamics (MD) simulation. Parameter space plots revealed unique MA'AT fits of the data, and root mean squared deviations (<0.2 Hz) were twofold smaller than those back-calculated from MD models, indicating that the MA'AT models better fit the experimental J-couplings. Context effects on both θ1 values were found to be small in a βGlcNAc-(1→4)-βGlcNAc disaccharide. MA'AT analysis gave a mean value of θ1 of 249° for αManNAc in H2O/2H2O, with a CSD of ∼19°, with both values in good agreement with MD. MA'AT models of N-acetyl side-chains are similar to those obtained previously for O-acetyl side-chains (J. Phys. Chem. B 2017, 121, 66-77). Both O- and N-acetylation conformationally constrain the C-O or C-N bonds relative to the same bonds in unsubstituted compounds. The present work confirms the ability of MA'AT analysis to reveal relatively small changes in mean molecular torsion angles in solution and provides additional evidence of the method as an experimental tool complementary to MD simulation.

Corrigendum to 'OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors'.

Corrigendum to 'OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors'.

Simulation studies of polypeptoids using replica exchange with dynamical scaling and dihedral biasing.

Polypeptoids differ from polypeptides in that the amide bond can more frequently adopt both cis and trans conformations. The transition between the two conformations requires overcoming a large energy barrier, making it difficult for conventional molecular simulations to adequately visit the cis and trans structures. A replica-exchange method is presented that allows for easy rotations of the amide bond and also an efficient linking to a high temperature replica. The method allows for just three replicas (one at the temperature and Hamiltonian of interest, a second high temperature replica with a biased dihedral potential, and a third connecting them) to overcome the amide bond sampling problem and also enhance sampling for other coordinates. The results indicate that for short peptoid oligomers, the conformations can range from all cis to all trans with an average cis/trans ratio that depends on side chain and potential model.

Expression of Concern to: OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors.

Expression of Concern to: OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors.

Concentration-Driven Self-Assembly of PS-b-PLA Bottlebrush Diblock Copolymers in Solution.

Bottlebrush polymers are a class of semiflexible, hierarchical macromolecules with unique potential for shape-, architecture-, and composition-based structure–property design. It is now well-established that in dilute to semidilute solution, bottlebrush homopolymers adopt a wormlike conformation, which decreases in extension (persistence length) as the concentration and molecular overlap increase. By comparison, the solution phase self-assembly of bottlebrush diblock copolymers (BBCP) in a good solvent remains poorly understood, despite critical relevance for solution processing of ordered phases and photonic crystals. In this work, we combine small-angle X-ray scattering, coarse-grained simulation, and polymer synthesis to map the equilibrium phase behavior and conformation of a set of large, nearly symmetric PS-b-PLA bottlebrush diblock copolymers in toluene. Three BBCP are synthesized, with side chains of number-averaged molecular weights of 4500 (PS) and 4200 g/mol (PLA) and total backbone degrees of polymerization of 100, 255, and 400 repeat units. The grafting density is one side chain per backbone repeat unit. With increasing concentration in solution, all three polymers progress through a similar structural transition: from dispersed, wormlike chains with concentration-dependent (decreasing) extension, through the onset of disordered PS/PLA compositional fluctuations, to the formation of a long-range ordered lamellar phase. With increasing concentration in the microphase-separated regimes, the domain spacing increases as individual chains partially re-extend due to block immiscibility. Increases in the backbone degree of polymerization lead to changes in the scattering profiles which are consistent with the increased segregation strength. Coarse-grained simulations using an implicit side-chain model are performed, and concentration-dependent self-assembly behavior is qualitatively matched to experiments. Finally, using the polymer with the largest backbone length, we demonstrate that lamellar phases develop a well-defined photonic band gap in solution, which can be tuned across the visible spectrum by varying polymer concentration.

Reactions of Arsenoplatin-1 with Protein Targets: A Combined Experimental and Theoretical Study.

Arsenoplatin-1 (AP-1) is a dual-action anticancer metallodrug with a promising pharmacological profile that features the simultaneous presence of a cisplatin-like center and an arsenite center. We investigated its interactions with proteins through a joint experimental and theoretical approach. The reactivity of AP-1 with a variety of proteins, including carbonic anhydrase (CA), superoxide dismutase (SOD), myoglobin (Mb), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), and human serum albumin (HSA), was analyzed by means of electrospray ionization mass spectrometry (ESI MS) measurements. In accordance with previous observations, ESI MS experiments revealed that the obtained metallodrug–protein adducts originated from the binding of the [(AP-1)-Cl]+ fragment to accessible protein residues. Remarkably, in two cases, i.e., Mb and GAPDH, the formation of a bound metallic fragment that lacked the arsenic center was highlighted. The reactions of AP-1 with various nucleophiles side chains of neutral histidine, methionine, cysteine, and selenocysteine, in neutral form as well as cysteine and selenocysteine in anionic form, were subsequently analyzed through a computational approach. We found that the aquation of AP-1 is energetically disfavored, with a reaction free energy of +19.2 kcal/mol demonstrating that AP-1 presumably attacks its biological targets through the exchange of the chloride ligand. The theoretical analysis of thermodynamics and kinetics for the ligand-exchange processes of AP-1 with His, Met, Cys, Sec, Cys–, and Sec– side chain models unveils that only neutral histidine and deprotonated cysteine and selenocysteine are able to effectively replace the chloride ligand in AP-1.

Combined in Silico and in Vitro Approaches To Uncover the Oxidation and Schiff Base Reaction of Baicalein as an Inhibitor of Amyloid Protein Aggregation.

The oxidized form of baicalein (BA) leads to covalent binding with human amyloid proteins. Such adducts hamper the aggregation and deposition of fibrils. A novel reaction of BA with pentylamine (PA) as a model for the lysine side chain is described. This is the first study addressing the atomistic details of a Schiff base reaction with the trihydroxylated moiety of BA. Nuclear magnetic resonance and mass spectrometry approaches clearly indicate the formation of dehydrobaicalein in solution as well as its condensation with PA under aerobic conditions, yielding regioselectively C6-substituted products. The combined results suggest initial ion pair formation between BA and PA, followed by a redox chain reaction: the initiation by oxygen/air; an o-quinone-based chain involving oxidation and reduction steps; and extra off-chain formation of a doubly oxidized product. These mechanistic details support the anti-amyloid activity of BA and endorse its trihydroxyphenyl moiety as a pharmacophore for drug-design studies.