Fast and accurate prediction of protein side-chain conformations

A Curated Rotamer Library for Common Post-Translational Modifications of Proteins

Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered proteins (IDPs). However much of protein function is modulated beyond the translated sequence through thFiguree introduction of post-translational modifications (PTMs). In this work we have provided a curated set of side chain rotamers for the most common PTMs derived from the RCSB PDB database, including phosphorylated, methylated, and acetylated sidechains. Our rotamer libraries improve upon existing methods such as SIDEpro and Rosetta in predicting the experimental structures for PTMs in folded proteins. In addition, we showcase our PTM libraries in full use by generating ensembles with the Monte Carlo Side Chain Entropy (MCSCE) for folded proteins, and combining MCSCE with the Local Disordered Region Sampling algorithms within IDPConformerGenerator for proteins with intrinsically disordered regions.

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 25 G 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.

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.

H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing

Accurately modeling protein 3D structure is essential for the design of functional proteins. An important sub-task of structure modeling is protein side-chain packing: predicting the conformation of side-chains (rotamers) given the protein's backbone structure and amino-acid sequence. Conventional approaches for this task rely on expensive sampling procedures over hand-crafted energy functions and rotamer libraries. Recently, several deep learning methods have been developed to tackle the problem in a data-driven way, albeit with vastly different formulations (from image-to-image translation to directly predicting atomic coordinates). Here, we frame the problem as a joint regression over the side-chains' true degrees of freedom: the dihedral χ angles. We carefully study possible objective functions for this task, while accounting for the underlying symmetries of the task. We propose Holographic Packer (H-Packer), a novel two-stage algorithm for side-chain packing built on top of two light-weight rotationally equivariant neural networks. We evaluate our method on CASP13 and CASP14 targets. H-Packer is computationally efficient and shows favorable performance against conventional physics-based algorithms and is competitive against alternative deep learning solutions.

An end-to-end deep learning method for protein side-chain packing and inverse folding

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

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.

Engineering the kinetic stability of a β-trefoil protein by tuning its topological complexity

Kinetic stability, defined as the rate of protein unfolding, is central to determining the functional lifetime of proteins, both in nature and in wide-ranging medical and biotechnological applications. Further, high kinetic stability is generally correlated with high resistance against chemical and thermal denaturation, as well as proteolytic degradation. Despite its significance, specific mechanisms governing kinetic stability remain largely unknown, and few studies address the rational design of kinetic stability. Here, we describe a method for designing protein kinetic stability that uses protein long-range order, absolute contact order, and simulated free energy barriers of unfolding to quantitatively analyze and predict unfolding kinetics. We analyze two β-trefoil proteins: hisactophilin, a quasi-three-fold symmetric natural protein with moderate stability, and ThreeFoil, a designed three-fold symmetric protein with extremely high kinetic stability. The quantitative analysis identifies marked differences in long-range interactions across the protein hydrophobic cores that partially account for the differences in kinetic stability. Swapping the core interactions of ThreeFoil into hisactophilin increases kinetic stability with close agreement between predicted and experimentally measured unfolding rates. These results demonstrate the predictive power of readily applied measures of protein topology for altering kinetic stability and recommend core engineering as a tractable target for rationally designing kinetic stability that may be widely applicable.

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.

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.

Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol

In recent years, photoactive proteins such as rhodopsins have become a common target for cutting-edge research in the field of optogenetics. Alongside wet-lab research, computational methods are also developing rapidly to provide the necessary tools to analyze and rationalize experimental results and, most of all, drive the design of novel systems. The Automatic Rhodopsin Modeling (ARM) protocol is focused on providing exactly the necessary computational tools to study rhodopsins, those being either natural or resulting from mutations. The code has evolved along the years to finally provide results that are reproducible by any user, accurate and reliable so as to replicate experimental trends. Furthermore, the code is efficient in terms of necessary computing resources and time, and scalable in terms of both number of concurrent calculations as well as features. In this review, we will show how the code underlying ARM achieved each of these properties.

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

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).

Benchmarking of structure refinement methods for protein complex models

Protein structure docking is the process in which the quaternary structure of a protein complex is predicted from individual tertiary structures of the protein subunits. Protein docking is typically performed in two main steps. The subunits are first docked while keeping them rigid to form the complex, which is then followed by structure refinement. Structure refinement is crucial for a practical use of computational protein docking models, as it is aimed for correcting conformations of interacting residues and atoms at the interface. Here, we benchmarked the performance of eight existing protein structure refinement methods in refinement of protein complex models. We show that the fraction of native contacts between subunits is by far the most straightforward metric to improve. However, backbone dependent metrics, based on the Root Mean Square Deviation proved more difficult to improve via refinement.

De novo protein design by an energy function based on series expansion in distance and orientation dependence

MOTIVATION

Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing.

RESULTS

The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design.

AVAILABILITY AND IMPLEMENTATION

The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps

The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.

OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods

Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sampling, we first propose a novel side-chain rotamer prediction method based on deep neural networks, named OPUS-RotaNN. Then, on the basis of our previous work OPUS-Rota2, we propose an open-source side-chain modeling framework, OPUS-Rota3, which integrates the results of different methods into its rotamer library as the sampling candidates. By including OPUS-RotaNN into OPUS-Rota3, we conduct our experiments on three native backbone test sets and one non-native backbone test set. On the native backbone test set, CAMEO-Hard61 for example, OPUS-Rota3 successfully predicts 51.14% of all side-chain dihedral angles with a tolerance criterion of 20° and outperforms OSCAR-star (50.87%), SCWRL4 (50.40%), and FASPR (49.85%). On the non-native backbone test set DB379-ITASSER, the accuracy of OPUS-Rota3 is 52.49%, better than OSCAR-star (48.95%), FASPR (48.69%), and SCWRL4 (48.29%). All the source codes including the training codes and the data we used are available at https://github.com/thuxugang/opus_rota3.

Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

MOTIVATION

Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models.

RESULTS

We present a novel single-model QA method called SBROD. The SBROD (Smooth Backbone-Reliant Orientation-Dependent) method uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models. The proposed method deduces its scoring function from a training set of protein models. The SBROD scoring function is composed of four terms related to different structural features: residue-residue orientations, contacts between backbone atoms, hydrogen bonding and solvent-solute interactions. It is smooth with respect to atomic coordinates and thus is potentially applicable to continuous gradient-based optimization of protein conformations. Furthermore, it can also be used for coarse-grained protein modeling and computational protein design. SBROD proved to achieve similar performance to state-of-the-art single-model QA methods on diverse datasets (CASP11, CASP12 and MOULDER).

AVAILABILITY AND IMPLEMENTATION

The standalone application implemented in C++ and Python is freely available at https://gitlab.inria.fr/grudinin/sbrod and supported on Linux, MacOS and Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

IDP-LZerD: Software for Modeling Disordered Protein Interactions

Modeling the tertiary structure of protein-protein interaction complex has been well studied over many years, especially in the case where the structures of both binding partners are roughly the same before and after binding. However, the assembly of complexes with less-ordered partners is a much harder problem, and modeling even small amounts of flexibility can pose a challenge. In an extreme case, where one of the binding partners is intrinsically disordered before binding, we have previously shown that by initially disregarding the coupling between windows of these intrinsically disordered proteins (IDPs), we can reliably assemble complexes involving IDPs up to at least 69 residues long. Here, we detail the use of the IDP-LZerD package and protocol.

Toward the Accuracy and Speed of Protein Side-Chain Packing: A Systematic Study on Rotamer Libraries

Protein rotamers refer to the conformational isomers taken by the side-chains of amino acids to accommodate specific structural folding environments. Since accurate modeling of atomic interactions is difficult, rotamer information collected from experimentally solved protein structures is often used to guide side-chain packing in protein folding and sequence design studies. Many rotamer libraries have been built in the literature but there is little quantitative guidance on which libraries should be chosen for different structural modeling studies. Here, we performed a comparative study of six widely used rotamer libraries and systematically examined their suitability for protein folding and sequence design in four aspects: (1) side-chain match accuracy, (2) side-chain conformation prediction, (3) de novo protein sequence design, and (4) computational time cost. We demonstrated that, compared to the backbone-dependent rotamer libraries (BBDRLs), the backbone-independent rotamer libraries (BBIRLs) generated conformations that more closely matched the native conformations due to the larger number of rotamers in the local rotamer search spaces. However, more practically, using an optimized physical energy function incorporated into a simulated annealing Monte Carlo searching scheme, we showed that utilization of the BBDRLs could result in higher accuracies in side-chain prediction and higher sequence recapitulation rates in protein design experiments. Detailed data analyses showed that the major advantage of BBDRLs lies in the energy term derived from the rotamer probabilities that are associated with the individual backbone torsion angle subspaces. This term is important for distinguishing between amino acid identities as well as the rotamer conformations of an amino acid. Meanwhile, the backbone torsion angle subspace-specific rotamer search drastically speeds up the searching time, despite the significantly larger number of total rotamers in the BBDRLs. These results should provide important guidance for the development and selection of rotamer libraries for practical protein design and structure prediction studies.

Computational reconstruction of atomistic protein structures from coarse-grained models

Three-dimensional protein structures, whether determined experimentally or theoretically, are often too low resolution. In this mini-review, we outline the computational methods for protein structure reconstruction from incomplete coarse-grained to all atomistic models. Typical reconstruction schemes can be divided into four major steps. Usually, the first step is reconstruction of the protein backbone chain starting from the C-alpha trace. This is followed by side-chains rebuilding based on protein backbone geometry. Subsequently, hydrogen atoms can be reconstructed. Finally, the resulting all-atom models may require structure optimization. Many methods are available to perform each of these tasks. We discuss the available tools and their potential applications in integrative modeling pipelines that can transfer coarse-grained information from computational predictions, or experiment, to all atomistic structures.

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

OPUS-Rota2: An Improved Fast and Accurate Side-Chain Modeling Method

Side-chain modeling plays a critical role in protein structure prediction. However, in many current methods, balancing the speed and accuracy is still challenging. In this paper, on the basis of our previous work OPUS-Rota (Protein Sci. 2008, 17, 1576-1585), we introduce a new side-chain modeling method, OPUS-Rota2, which is tested on both a 65-protein test set (DB65) in the OPUS-Rota paper and a 379-protein test set (DB379) in the SCWRL4 paper. If the main chain is native, OPUS-Rota2 is more accurate than OPUS-Rota, SCWRL4, and OSCAR-star but slightly less accurate than OSCAR-o. Also, if the main chain is non-native, OPUS-Rota2 is more accurate than any other method. Moreover, OPUS-Rota2 is significantly faster than any other method, in particular, 2 orders of magnitude faster than OSCAR-o. Thus, the combination of higher accuracy and speed of OPUS-Rota2 in modeling side chains on both the native and non-native main chains makes OPUS-Rota2 a very useful tool in protein structure modeling.

Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics

To address the large gap between time scales that can be easily reached by molecular simulations and those required to understand protein dynamics, we present a rapid self-consistent approximation of the side chain free energy at every integration step. In analogy with the adiabatic Born-Oppenheimer approximation for electronic structure, the protein backbone dynamics are simulated as preceding according to the dictates of the free energy of an instantaneously-equilibrated side chain potential. The side chain free energy is computed on the fly, allowing the protein backbone dynamics to traverse a greatly smoothed energetic landscape. This computation results in extremely rapid equilibration and sampling of the Boltzmann distribution. Our method, termed Upside, employs a reduced model involving the three backbone atoms, along with the carbonyl oxygen and amide proton, and a single (oriented) side chain bead having multiple locations reflecting the conformational diversity of the side chain's rotameric states. We also introduce a novel, maximum-likelihood method to parameterize the side chain interactions using protein structures. We demonstrate state-of-the-art accuracy for predicting χ1 rotamer states while consuming only milliseconds of CPU time. Our method enables rapidly equilibrating coarse-grained simulations that can nonetheless contain significant molecular detail. We also show that the resulting free energies of the side chains are sufficiently accurate for de novo folding of some proteins.

Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking

Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.

Protein side-chain packing problem: is there still room for improvement?

The protein side-chain packing problem (PSCPP) is an important subproblem of both protein structure prediction and protein design. During the past two decades, a large number of methods have been proposed to tackle this problem. These methods consist of three main components: a rotamer library, a scoring function and a search strategy. The average overall accuracy level obtained by these methods is approximately 87%. Whether a better accuracy level could be achieved remains to be answered. To address this question, we calculated the maximum accuracy level attainable using a simple rotamer library, independently of the energy function or the search method. Using 2883 different structures from the Protein Data Bank, we compared this accuracy level with the accuracy level of five state-of-the-art methods. These comparisons indicated that, for buried residues in the protein, we are already close to the best possible accuracy results. In addition, for exposed residues, we found that a significant gap exists between the possible improvement and the maximum accuracy level achievable with current methods. After determining that an improvement is possible, the next step is to understand what limitations are preventing us from obtaining such an improvement. Previous works on protein structure prediction and protein design have shown that scoring function inaccuracies may represent the main obstacle to achieving better results for these problems. To show that the same is true for the PSCPP, we evaluated the quality of two scoring functions used by some state-of-the-art algorithms. Our results indicate that neither of these scoring functions can guide the search method correctly, thereby reinforcing the idea that efforts to solve the PSCPP must also focus on developing better scoring functions.

Scoring of Side-Chain Packings: An Analysis of Weight Factors and Molecular Dynamics Structures

The protein side-chain packing problem (PSCPP) is a central task in computational protein design. The problem is usually modeled as a combinatorial optimization problem, which consists of searching for a set of rotamers, from a given rotamer library, that minimizes a scoring function (SF). The SF is a weighted sum of terms, that can be decomposed in physics-based and knowledge-based terms. Although there are many methods to obtain approximate solutions for this problem, all of them have similar performances and there has not been a significant improvement in recent years. Studies on protein structure prediction and protein design revealed the limitations of current SFs to achieve further improvements for these two problems. In the same line, a recent work reported a similar result for the PSCPP. In this work, we ask whether or not this negative result regarding further improvements in performance is due to (i) an incorrect weighting of the SFs terms or (ii) the constrained conformation resulting from the protein crystallization process. To analyze these questions, we (i) model the PSCPP as a bi-objective combinatorial optimization problem, optimizing, at the same time, the two most important terms of two SFs of state-of-the-art algorithms and (ii) performed a preprocessing relaxation of the crystal structure through molecular dynamics to simulate the protein in the solvent and evaluated the performance of these two state-of-the-art SFs under these conditions. Our results indicate that (i) no matter what combination of weight factors we use the current SFs will not lead to better performances and (ii) the evaluated SFs will not be able to improve performance on relaxed structures. Furthermore, the experiments revealed that the SFs and the methods are biased toward crystallized structures.

Antibody Affinity Maturation by Computational Design

The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.

Collective repacking reveals that the structures of protein cores are uniquely specified by steric repulsive interactions

Protein core repacking is a standard test of protein modeling software. A recent study of six different modeling software packages showed that they are more successful at predicting side chain conformations of core compared to surface residues. All the modeling software tested have multicomponent energy functions, typically including contributions from solvation, electrostatics, hydrogen bonding and Lennard–Jones interactions in addition to statistical terms based on observed protein structures. We investigated to what extent a simplified energy function that includes only stereochemical constraints and repulsive hard-sphere interactions can correctly repack protein cores. For single residue and collective repacking, the hard-sphere model accurately recapitulates the observed side chain conformations for Ile, Leu, Phe, Thr, Trp, Tyr and Val. This result shows that there are no alternative, sterically allowed side chain conformations of core residues. Analysis of the same set of protein cores using the Rosetta software suite revealed that the hard-sphere model and Rosetta perform equally well on Ile, Leu, Phe, Thr and Val; the hard-sphere model performs better on Trp and Tyr and Rosetta performs better on Ser. We conclude that the high prediction accuracy in protein cores obtained by protein modeling software and our simplified hard-sphere approach reflects the high density of protein cores and dominance of steric repulsion.

Modeling disordered protein interactions from biophysical principles

Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.

A substantial fraction of the proteins encoded in genomes are intrinsically disordered proteins (IDPs), which lack a single stable structure in the native state. IDPs serve many functions including mediating protein-protein interactions (PPIs). Such disordered PPIs are prevalent in important regulatory pathways, including many interactions of the tumor suppressor protein p53. To elucidate the molecular mechanisms of disordered PPIs, obtaining tertiary structure information is essential; however, they are difficult to study with experimental techniques and existing computational protein-protein and protein-peptide modeling methods are unable to model disordered PPIs. Here we present a novel computational method for modeling the structure of disordered PPIs, which is the first of this sort. The method, IDP-LZerD, is designed to follow a known biophysical picture of the mechanism of how IDPs interact with structured proteins. IDP-LZerD successfully modeled the majority of disordered PPIs tested. This technique opens up new possibilities for structural studies of IDPs and their interactions.

PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex

Structure determination of linear peptides of 5–50 amino acids in aqueous solution and interacting with proteins is a key aspect in structural biology. PEP-FOLD3 is a novel computational framework, that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance. PEP-FOLD3 is fast, and usually returns solutions in a few minutes. Testing PEP-FOLD3 on 56 peptides in aqueous solution led to experimental-like conformations for 80% of the targets. Using a benchmark of 61 peptide–protein targets starting from the unbound form of the protein receptor, PEP-FOLD3 was able to generate peptide poses deviating on average by 3.3Å from the experimental conformation and return a native-like pose in the first 10 clusters for 52% of the targets. PEP-FOLD3 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3.

Protein side chain conformation predictions with an MMGBSA energy function

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc.

Structural basis for the regulation of enzymatic activity of Regnase-1 by domain-domain interactions

Regnase-1 is an RNase that directly cleaves mRNAs of inflammatory genes such as IL-6 and IL-12p40, and negatively regulates cellular inflammatory responses. Here, we report the structures of four domains of Regnase-1 from Mus musculus-the N-terminal domain (NTD), PilT N-terminus like (PIN) domain, zinc finger (ZF) domain and C-terminal domain (CTD). The PIN domain harbors the RNase catalytic center; however, it is insufficient for enzymatic activity. We found that the NTD associates with the PIN domain and significantly enhances its RNase activity. The PIN domain forms a head-to-tail oligomer and the dimer interface overlaps with the NTD binding site. Interestingly, mutations blocking PIN oligomerization had no RNase activity, indicating that both oligomerization and NTD binding are crucial for RNase activity in vitro. These results suggest that Regnase-1 RNase activity is tightly controlled by both intramolecular (NTD-PIN) and intermolecular (PIN-PIN) interactions.

Design of a protein tag and fluorogenic probe with modular structure for live-cell imaging of intracellular proteins

Conditional fluorescence imaging is a powerful technique for precise spatiotemporal analysis of proteins in live cells upon administration of a synthetic probe. To be applicable to various biological phenomena, probes must be membrane-permeable, have a high signal-to-noise ratio, and work quickly. To date, few probes meet all of these requirements. Here, we designed a fluorogenic probe (AcFCANB) that could label intracellular proteins fused to the photoactive yellow protein (PYP) tag in live cells within 30 min and used it to image heterochromatin protein 1 localization in nuclei. AcFCANB is based on a modular platform consisting of fluorophore, ligand and quencher. We accelerated the labeling reaction by strategic mutations of charged residues on the surface of PYP. A simple model based on molecular dynamics simulations quantitatively reproduced the cooperative effect of multiple mutations on labeling rate.

RabGDIα is a negative regulator of interferon-γ-inducible GTPase-dependent cell-autonomous immunity to Toxoplasma gondii

IFN-γ orchestrates cell-autonomous host defense against various intracellular vacuolar pathogens. IFN-γ-inducible GTPases, such as p47 immunity-related GTPases (IRGs) and p65 guanylate-binding proteins (GBPs), are recruited to pathogen-containing vacuoles, which is important for disruption of the vacuoles, culminating in the cell-autonomous clearance. Although the positive regulation for the proper recruitment of IRGs and GBPs to the vacuoles has been elucidated, the suppressive mechanism is unclear. Here, we show that Rab GDP dissociation inhibitor α (RabGDIα), originally identified as a Rab small GTPase inhibitor, is a negative regulator of IFN-γ-inducible GTPases in cell-autonomous immunity to the intracellular pathogen Toxoplasma gondii. Overexpression of RabGDIα, but not of RabGDIβ, impaired IFN-γ-dependent reduction of T. gondii numbers. Conversely, RabGDIα deletion in macrophages and fibroblasts enhanced the IFN-γ-induced clearance of T. gondii. Furthermore, upon a high dose of infection by T. gondii, RabGDIα-deficient mice exhibited a decreased parasite burden in the brain and increased resistance in the chronic phase than did control mice. Among members of IRGs and GBPs important for the parasite clearance, Irga6 and Gbp2 alone were more frequently recruited to T. gondii-forming parasitophorous vacuoles in RabGDIα-deficient cells. Notably, Gbp2 positively controlled Irga6 recruitment that was inhibited by direct and specific interactions of RabGDIα with Gbp2 through the lipid-binding pocket. Taken together, our results suggest that RabGDIα inhibits host defense against T. gondii by negatively regulating the Gbp2-Irga6 axis of IFN-γ-dependent cell-autonomous immunity.

Assessment of protein side-chain conformation prediction methods in different residue environments

Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic-detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers.

Kotai Antibody Builder: automated high-resolution structural modeling of antibodies

MOTIVATION

Kotai Antibody Builder is a Web service for tertiary structural modeling of antibody variable regions. It consists of three main steps: hybrid template selection by sequence alignment and canonical rules, 3D rendering of alignments and CDR-H3 loop modeling. For the last step, in addition to rule-based heuristics used to build the initial model, a refinement option is available that uses fragment assembly followed by knowledge-based scoring. Using targets from the Second Antibody Modeling Assessment, we demonstrate that Kotai Antibody Builder generates models with an overall accuracy equal to that of the best-performing semi-automated predictors using expert knowledge.

AVAILABILITY AND IMPLEMENTATION

Kotai Antibody Builder is available at http://kotaiab.org

CONTACT

standley@ifrec.osaka-u.ac.jp.

Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures

MOTIVATION

Accurately predicting protein side-chain conformations is an important subproblem of the broader protein structure prediction problem. Several methods exist for generating fairly accurate models for moderate-size proteins in seconds or less. However, a major limitation of these methods is their inability to model post-translational modifications (PTMs) and unnatural amino acids. In natural living systems, the chemical groups added following translation are often critical for the function of the protein. In engineered systems, unnatural amino acids are incorporated into proteins to explore structure-function relationships and create novel proteins.

RESULTS

We present a new version of SIDEpro to predict the side chains of proteins containing non-standard amino acids, including 15 of the most frequently observed PTMs in the Protein Data Bank and all types of phosphorylation. SIDEpro uses energy functions that are parameterized by neural networks trained from available data. For PTMs, the [Formula: see text] and [Formula: see text] accuracies are comparable with those obtained for the precursor amino acid, and so are the RMSD values for the atoms shared with the precursor amino acid. In addition, SIDEpro can accommodate any PTM or unnatural amino acid, thus providing a flexible prediction system for high-throughput modeling of proteins beyond the standard amino acids.

AVAILABILITY AND IMPLEMENTATION

SIDEpro programs and Web server, rotamer libraries and data are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu/

LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains

Prediction of protein loop conformations without any prior knowledge (ab initio prediction) is an unsolved problem. Its solution will significantly impact protein homology and template-based modeling as well as ab initio protein-structure prediction. Here, we developed a coarse-grained, optimized scoring function for initial sampling and ranking of loop decoys. The resulting decoys are then further optimized in backbone and side-chain conformations and ranked by all-atom energy scoring functions. The final integrated technique called loop prediction by energy-assisted protocol achieved a median value of 2.1 Å root mean square deviation (RMSD) for 325 12-residue test loops and 2.0 Å RMSD for 45 12-residue loops from critical assessment of structure-prediction techniques (CASP) 10 target proteins with native core structures (backbone and side chains). If all side-chain conformations in protein cores were predicted in the absence of the target loop, loop-prediction accuracy only reduces slightly (0.2 Å difference in RMSD for 12-residue loops in the CASP target proteins). The accuracy obtained is about 1 Å RMSD or more improvement over other methods we tested. The executable file for a Linux system is freely available for academic users at http://sparks-lab.org.

PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides

In the context of the renewed interest of peptides as therapeutics, it is important to have an on-line resource for 3D structure prediction of peptides with well-defined structures in aqueous solution. We present an updated version of PEP-FOLD allowing the treatment of both linear and disulphide bonded cyclic peptides with 9-36 amino acids. The server makes possible to define disulphide bonds and any residue-residue proximity under the guidance of the biologists. Using a benchmark of 34 cyclic peptides with one, two and three disulphide bonds, the best PEP-FOLD models deviate by an average RMS of 2.75 Å from the full NMR structures. Using a benchmark of 37 linear peptides, PEP-FOLD locates lowest-energy conformations deviating by 3 Å RMS from the NMR rigid cores. The evolution of PEP-FOLD comes as a new on-line service to supersede the previous server. The server is available at: http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD.

Protein Loop Modeling with Optimized Backbone Potential Functions

We represented protein backbone potential as a Fourier series. The parameters of the backbone dihedral potential were initialized to random values and optimized by Monte Carlo simulations so that generated native-like loop decoys had a lower energy than non-native decoys. The low energy regions of the optimized backbone potential were consistent with observed Ramachandran plots derived from crystal structures. The backbone potential was then used for the prediction of loop conformations (OSCAR-loop) combining with the previously described OSCAR force field, which has been shown to be very accurate in side chain modeling. As a result, the accuracy of OSCAR-loop was improved by local energy minimization based on the complete force field. The average accuracies were 0.40, 0.70, 1.10, 2.08, and 3.58 Å for 4, 6, 8, 10, and 12-residue loops, respectively, with each size being represented by 325 to 2809 targets. The accuracy was better than that of other loop modeling algorithms for short loops (<10 residues). For longer loops, the prediction accuracy was improved by concurrently sampling with a fragment-based method, Spanner. OSCAR-loop is available for download at http://sysimm.ifrec.osaka-u.ac.jp/OSCAR/ .