Prediction of the structures of proteins with the UNRES force field, including dynamic formation and breaking of disulfide bonds

The presence of disulfide bonds is essential for maintaining the structure and function of many proteins. The disulfide bonds are usually formed dynamically during folding. This process is not accounted for in present algorithms for protein-structure prediction, which either deduce the possible positions of disulfide bonds only after the structure is formed or assume fixed disulfide bonds during the course of simulated folding. In this work, the conformational space annealing (CSA) method and the UNRES united-residue force field were extended to treat dynamic formation of disulfide bonds. A harmonic potential is imposed on the distance between disulfide-bonded cysteine side-chain centroids to describe the energetics of bond distortion and an energy gain of 5.5 kcal/mol is added for disulfide-bond formation. Formation, breaking and rearrangement of disulfide bonds are included in the CSA search by introducing appropriate operations; the search can also be carried out with a fixed disulfide-bond arrangement. The algorithm was applied to four proteins: 1EI0 (alpha), 1NKL (alpha), 1L1I (beta-helix) and 1ED0 (alpha + beta). For 1EI0, a low-energy structure with correct fold was obtained both in the runs without and with disulfide bonds; however, it was obtained as the lowest in energy only with the native disulfide-bond arrangement. For the other proteins studied, structures with the correct fold were obtained as the lowest (1NKL and 1L1I) or low-energy structures (1ED0) only in runs with disulfide bonds, although the final disulfide-bond arrangement was non-native. The results demonstrate that, by including the possibility of formation of disulfide bonds, the predictive power of the UNRES force field is enhanced, even though the disulfide-bond potential introduced here rarely produces disulfide bonds in native positions. To the best of our knowledge, this is the first algorithm for energy-based prediction of the structure of disulfide-bonded proteins without any assumption as to the positions of native disulfides or human intervention. Directions for improving the potentials and the search method are suggested.     

Improved conformational space annealing method to treat β-structure with the UNRES force-field and to enhance scalability of parallel implementation

Successful application of physics-based protein-structure prediction methods depends on sophisticated computational approaches to global optimization of the conformational energy of a polypeptide chain. One of the most effective procedures for the global optimization of protein structures appears to be the Conformational Space Annealing (CSA) method. CSA is a hybrid method which combines genetic algorithms, essential aspects of the build-up method and a local gradient-based minimization. CSA evolves the population of conformations through genetic operators (mutations, i.e. perturbations of selected geometric parameters, and crossovers, i.e. exchange of selected subsets of geometric parameters between conformations) to a final population optimizing their conformational energy. Implementation of the CSA method with the united-residue force field (UNRES, in which each amino-acid residue is represented by two interaction sites, namely the united peptide group and the united side-chain) was enhanced by introducing new crossover operations consisting of (i) copying β-hairpins, (ii) copying remote strand pairs forming non-local β-sheets, and (iii) copying α-helical segments. A mutation operation, which shifts the position of a β-turn, was also introduced. The new operations promote β-structure, and are essential for searching the conformational space of proteins containing both α- and β-structure; without these operations, excessive preference of α-helical structures is obtained, even though these structures are high in energy. Parallelization of the CSA method has also been enhanced by removing most of the synchronization steps; the improved algorithm scales almost linearly up to 1,000 processors with over 75% average performance.     

Distance-constrained molecular docking by simulated annealing

An optimized method based on the principle of simulated annealingis presented for determining the relative position and orientationof interacting molecules. The spatial relationships of thesemolecules are described by intermolecular distance constraintsbetween specific pairs of atoms, such as found in hydrogen bondsor from experimentally determined data. The method makes useof a random walk through six rotational and translational degreesof freedom where the constituent molecules are treated as rigidbodies. Van der Waals repulsions are used only to define a lowerbound on distances between constrained atom pairs within thedocking procedure. A cost function comprised of purely geometricconstraints is optimized via simulated annealing, in order tosearch for the best orientation and position of the two molecules.Our docking procedure is applied to eight serine proteinasecomplexes from the Brookhaven Protein Data Bank. For each simulation100 computations were performed. A typical docking computationrequires only a few seconds of CPU time on a VAXserver 3500.The influence of the number of constraints on the final dockedpositions was studied. The sensitivity of the docking procedureto a ligand structure which is not well defined is also addressed.Possible applications of this method include using approximatedistances incorporating complete energy functions.     

SwarmDock and the Use of Normal Modes in Protein-Protein Docking

Here is presented an investigation of the use of normal modes in protein-protein docking, both in theory and in practice. Upper limits of the ability of normal modes to capture the unbound to bound conformational change are calculated on a large test set, with particular focus on the binding interface, the subset of residues from which the binding energy is calculated. Further, the SwarmDock algorithm is presented, to demonstrate that the modelling of conformational change as a linear combination of normal modes is an effective method of modelling flexibility in protein-protein docking.     

Scoring function based on weighted residue network

Molecular docking is an important method for the research of protein-protein interaction and recognition. A protein can be considered as a network when the residues are treated as its nodes. With the contact energy between residues as link weight, a weighted residue network is constructed in this paper. Two weighted parameters (strength and weighted average nearest neighbors' degree) are introduced into this model at the same time. The stability of a protein is characterized by its strength. The global topological properties of the protein-protein complex are reflected by the weighted average nearest neighbors' degree. Based on this weighted network model and these two parameters, a new docking scoring function is proposed in this paper. The scoring and ranking for 42 systems' bound and unbounded docking results are performed with this new scoring function. Comparing the results obtained from this new scoring function with that from the pair potentials scoring function, we found that this new scoring function has a similar performance to the pair potentials on some items, and this new scoring function can get a better success rate. The calculation of this new scoring function is easy, and the result of its scoring and ranking is acceptable. This work can help us better understand the mechanisms of protein-protein interactions and recognition.     

改进的自适应模拟退火算法及其在过程综合中的应用

为有效解决化工过程综合中的MINLP问题，针对连续变量的模拟退火算法搜索慢的缺点，提出了一种改进的自适应模拟退火算法（Adaptive Simulated Algorithms,ASA)，采取自适应调整温度和搜索步长两种策略，大大加快搜索速度，提高最优解的质量。实算结果充分体现了所提出算法的优点，并很好地应用于化工过程综合问题。     

基于一类混合PSO算法的函数优化与模型降阶研究

为了克服传统微粒群优化(PSO)算法容易早熟收敛和陷入局部极小的缺点,通过对PSO算法特点和行为的分析,提出一类有机结合模拟退火(SA)算法和PSO算法的混合算法.混合算法不仅利用PSO的机制进行群体全局搜索,而且利用模拟退火的思想恰当地选择微粒的最好历史位置,保障了群体多样性,并有效平衡了算法的探索和趋化能力,进而改善了算法的优化性能.基于典型复杂函数优化问题和模型降阶问题的仿真结果表明,所提混合算法具有很好的优化质量、搜索效率和鲁棒性.     

A Generic Force Field for Protein Coarse-Grained Molecular Dynamics Simulation

Coarse-grained (CG) force fields have become promising tools for studies of protein behavior, but the balance of speed and accuracy is still a challenge in the research of protein coarse graining methodology. In this work, 20 CG beads have been designed based on the structures of amino acid residues, with which an amino acid can be represented by one or two beads, and a CG solvent model with five water molecules was adopted to ensure the consistence with the protein CG beads. The internal interactions in protein were classified according to the types of the interacting CG beads, and adequate potential functions were chosen and systematically parameterized to fit the energy distributions. The proposed CG force field has been tested on eight proteins, and each protein was simulated for 1000 ns. Even without any extra structure knowledge of the simulated proteins, the Cα root mean square deviations (RMSDs) with respect to their experimental structures are close to those of relatively short time all atom molecular dynamics simulations. However, our coarse grained force field will require further refinement to improve agreement with and persistence of native-like structures. In addition, the root mean square fluctuations (RMSFs) relative to the average structures derived from the simulations show that the conformational fluctuations of the proteins can be sampled.     

5′-鸟苷酸的晶体结构预测

采用Cerius2软件中的Polymorph模块,预测5′-鸟苷酸的晶体结构。采用蒙特卡罗模拟退火方法在晶格能超曲面上搜索晶体可能的堆垛方式;基于堆垛相似性,有选择性的将这些可能的结构划分成各个特征组;在所有自由度上对每一特征结构进行优化;对优化后的各个结构再次分组排除重复;根据晶格能对最后得到的结构进行排序,得到5′-鸟苷酸最可能的空间群为P212121。     

In Silico Discovery of Anticancer Peptides from Sanghuang

Anticancer peptide (ACP) is a short peptide with less than 50 amino acids that has been discovered in a variety of foods. It has been demonstrated that traditional Chinese medicine or food can help treat cancer in some cases, which suggests that ACP may be one of the therapeutic ingredients. Studies on the anti-cancer properties of Sanghuangporus sanghuang have concentrated on polysaccharides, flavonoids, triterpenoids, etc. The function of peptides has not received much attention. The purpose of this study is to use computer mining techniques to search for potential anticancer peptides from 62 proteins of Sanghuang. We used mACPpred to perform sequence scans after theoretical trypsin hydrolysis and discovered nine fragments with an anticancer probability of over 0.60. The study used AlphaFold 2 to perform structural modeling of the first three ACPs discovered, which had blast results from the Cancer PPD database. Using reverse docking technology, we found the target proteins and interacting residues of two ACPs with an unknown mechanism. Reverse docking results predicted the binding modes of the ACPs and their target protein. In addition, we determined the active part of ACPs by quantum chemical calculation. Our study provides a framework for the future discovery of functional peptides from foods. The ACPs discovered have the potential to be used as drugs in oncology clinical treatment after further research.     

多产品间歇化工过程最优设计──混合模拟退火法

将模拟退火法（SimulatedAnnealing）与启发法（Heuristics）相结合,得到一种混合优化算法,应用于多产品间歇化工过程最优设计中,效果很好.该算法利用模拟退火的全局最优性保证了算法的全局最优,利用启发法加快了局部寻优进程,具有计算速度快、收敛性好等优点．同时,该算法自动确定了中间罐的最优数量和位置．算法对初值要求低,所需内存小,与严格数学规划法相比,相对误差小于0.5％．     

Refinement of protein cores and protein-peptide interfaces using a potential scaling approach

Refinement of side chain conformations in protein model structures and at the interface of predicted protein-protein or protein-peptide complexes is an important step during protein structural modelling and docking. A common approach for side chain prediction is to assume a rigid protein main chain for both docking partners and search for an optimal set of side chain rotamers to optimize the steric fit. However, depending on the target-template similarity in the case of comparative protein modelling and on the accuracy of an initially docked complex, the main chain template structure is only an approximation of a realistic target main chain. An inaccurate rigid main chain conformation can in turn interfere with the prediction of side chain conformations. In the present study, a potential scaling approach (PS-MD) during a molecular dynamics (MD) simulation that also allows the inclusion of explicit solvent has been used to predict side chain conformations on semi-flexible protein main chains. The PS-MD method converges much faster to realistic protein-peptide interface structures or protein core structures than standard MD simulations. Depending on the accuracy of the protein main chain, it also gives significantly better results compared with the standard rotamer search method.     

The identification of novel cyclic AMP-dependent protein kinase anchoring proteins using bioinformatic filters and peptide arrays

A-kinase anchoring proteins (AKAPs) localize cyclic AMP-dependent protein kinase (PKA) to specific regions in the cell and place PKA in proximity to its phosphorylation targets. A computational model was created to identify AKAPs that bind to the docking/dimerization domain of the RII alpha isoform of the regulatory subunit of PKA. The model was used to search the entire human proteome, and the top candidates were tested for an interaction using peptide array experiments. Verified interactions include sphingosine kinase interacting protein and retinoic acid-induced protein 16. These interactions highlight new signaling pathways mediated by PKA.     

A simple protein folding algorithm using a binary code and secondary structure constraints

We describe an algorithm to predict tertiary structures of smallproteins. In contrast to most current folding algorithms, ituses very few energy parameters. Given the secondary structuralelements in the sequence—-helices and ß-strands—thealgorithm searches -the remaining conformational space of asimplified real-space representation of chains to find a minimumenergy of an exceedingly simple potential function. The potentialis based only on a single type of favorable interaction betweenhydrophobic residues, an unfavorable excluded volume term ofspatial overlaps and, for sheet proteins, an interstrand hydrogenbond interaction. Where appropriate, the known disulfide bondsare constrained by a square-law potential. Conformations aresearched by a genetic algorithm. The model predicts reasonablywell the known tertiary folds of seven out of the 10 small proteinswe consider. We draw two conclusions. First, for the proteinswe tested, this exceedingly simple potential function is noworse than others having hundreds of energy parameters in findingthe right general tertiary structures. Second, despite its simplicity,the potential function is not the weak link in this algorithm.Differences between our predicted structures and the correcttargets can be ascribed to shortcomings in our search strategy.This potential function may be useful for testing other conformationalsearch strategies.     

Protein function microarrays based on self-immobilizing and self-labeling fusion proteins

Protein microarrays are an attractive approach for the high-throughput analysis of protein function, but their impact on proteomics has been limited by the technical difficulties associated with their generation. Here we demonstrate that fusion proteins of O6-alkylguanine-DNA alkyltransferase (AGT) can be used for the simple and reliable generation of protein microarrays for the analysis of protein function. Important features of the approach are the selectivity of the covalent immobilization; this allows for direct immobilization of proteins out of cell extracts, and the option both to label and to immobilize AGT fusion proteins, which allows for direct screening for protein-protein interactions between different AGT fusion proteins. In addition to the identification of protein-protein interactions, AGT-based protein microarrays can be used for the characterization of small molecule-protein interactions or post-translational modifications. The potential of the approach was demonstrated by investigating the post-translational modification of acyl carrier protein (ACP) from E. coli by different phosphopantetheine transferases (PPTases), yielding insights into the role of selected ACP amino acids in the ACP-PPTase interaction.     

The structural basis for docking in modular polyketide biosynthesis

Polyketide natural products such as erythromycin and rapamycin are assembled on polyketide synthases (PKSs), which consist of modular sets of catalytic activities distributed across multiple protein subunits. Correct protein-protein interactions among the PKS subunits which are critical to the fidelity of biosynthesis are mediated in part by "docking domains" at the termini of the proteins. The NMR solution structure of a representative docking domain complex from the erythromycin PKS (DEBS) was recently solved, and on this basis it has been proposed that PKS docking is mediated by the formation of an intermolecular four-alpha-helix bundle. Herein, we report the genetic engineering of such a docking domain complex by replacement of specific helical segments and analysis of triketide synthesis by mutant PKSs in vivo. The results of these helix swaps are fully consistent with the model and highlight residues in the docking domains that may be targeted to alter the efficiency or specificity of subunit-subunit docking in hybrid PKSs.     

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.     

Probing single-molecule protein conformational dynamics

Protein conformational fluctuations and dynamics, often complex and associated with inhomogeneities, play a crucial role in biomolecular functions. It is extremely difficult to characterize such inhomogeneous dynamics in an ensemble-averaged measurement, especially when the proteins are involved in multiple-step, multiple-conformation complex chemical interactions and transformations, such as in enzymatic reactions, protein-protein interactions, and ion-channel membrane protein processes. Alternatively, single-molecule spectroscopy is a powerful approach to probing and analyzing protein conformational dynamics in real time.     

Incorporating intermolecular distance into protein-protein docking

In this work, intermolecular distance was integrated into the docking of protein-protein complexes. To develop an efficient docking procedure, 22 enzyme-inhibitor targets and 15 antibody-antigen targets were taken from a benchmark set. A three-step approach was adopted, which included global sampling by FTDOCK, filtering by intermolecular distance and ranking by a composite scoring function. For the enzyme-inhibitor targets, the composite scoring function consists of geometry and energy terms. In the set composed of the approximately 100 highest ranked candidates for each target, correct complexes were identified for all of the 22 enzyme-inhibitor targets. This docking strategy also succeeded on the four test targets, of which three are CAPRI targets with the same receptor but different binding modes. Interestingly, all three binding modes were correctly predicted. For the antibody-antigen targets, CDR and physical energy were also used in the filtering process and informatics terms were added to the scoring function. The composite score had successful prediction for 13 of the 15 antibody-antigen targets.     

Fullerene sorting proteins

Proteins bind fullerenes. Hydrophobic pockets can accommodate a carbon cage either in full or in part. However, the identification of proteins able to discriminate between different cages is an open issue. Prediction of candidates able to perform this function is desirable and is achieved with an inverse docking procedure that accurately accounts for (i) van der Waals interactions between the cage and the protein surface, (ii) desolvation free energy, (iii) shape complementarity, and (iv) minimization of the number of steric clashes through conformational variations. A set of more than 1000 protein structures is divided into four categories that either select C(60) or C(70) (p-C(60) or p-C(70)) and either accommodate the cages in the same pocket (homosaccic proteins, from σακκoζ meaning pocket) or in different pockets (heterosaccic proteins). In agreement with the experiments, the KcsA Potassium Channel is predicted to have one of the best performances for both cages. Possible ways to exploit the results and efficiently separate the two cages with proteins are also discussed.