新型平均势函数在蛋白质反向折叠中的应用

利用蛋白质主链的极性分数及主链二面角为参量,构建了一种基于蛋白质结构数据库的势函数。将该势函数应用于蛋白质反向折叠研究中,发现该函数可成功地将蛋白质分子的天然构象从构建的构象库中识别出来;将一目标序列与构象库的每一可能的构象匹配,并用该势函数计算相应的能量,结果表明对绝大多数蛋白质分子来说,天然的构象的能量值总是最低。此外,该函数还将一些序列相似性较低,而结构相似性较高的蛋白质分子识别出来。我们认     

蛋白质- 核酸对接方法研究进展

分子对接技术作为预测蛋白质-核酸复合物结构的有效方法,为研究在生物学过程中蛋白质-核酸的相互作用提供了重要的工具。本文首先分析了当前蛋白质-核酸对接研究中的主要困难,例如构象变化和核糖磷酸骨架的带电性问题。然后从构象搜索、打分函数、柔性策略三个方面比较和总结了蛋白质-核酸对接中主要的计算方法。最后回顾了蛋白质-核酸对接计算模型的应用,并对未来的工作进行了展望。     

蛋白质-蛋白质分子对接方法中分子柔性处理与近天然结构筛选的研究

蛋白质-蛋白质分子对接方法是研究蛋白质分子间相互作用与识别的重要理论方法。该方法主要涉及复合物结合模式的构象搜索和近天然结构的筛选两个问题。在构象搜索中,分子柔性的处理是重点也是难点,围绕这一问题,近年来提出了许多新的方法。针对近天然结构的筛选问题,目前主要采用三种解决策略:结合位点信息的利用、相似结构的聚类和打分函数对结构的评价。本文围绕以上问题,就国内外研究进展和本研究小组的工作作详细的综述,并对进一步的研究方向进行了展望。     

预测蛋白质—蛋白质复合物结构的软对接算法

提出了一种有效的软对接算法 ,用于在已知受体和配体三维结构的条件下预测蛋白质 蛋白质复合物的结构。该算法的分子模型基于Janin提出的简化蛋白质模型 ,并在此基础上有所改进。对蛋白质分子表面的柔性氨基酸残基Arg、Lys、Asp、Glu和Met进行了特殊处理 ,通过软化分子表面的方式考虑了它们的侧链柔性。采用双重过滤技术来排除不合理的对接结构 ,此过滤技术是以复合物界面几何互补性和残基成对偏好性为标准提出的。对所得到的构象进行能量优化 ,之后用打分函数对这些结构进行排序 ,挑选出与复合物天然结构接近的构象。该打分函数包括静电、疏水和范德华相互作用能。用此算法对 2 6个复合物进行了结构预测 ,均找到了近天然结构 ,其中有 2 0个复合物的近天然结构排在了前 10位。改进的分子模型可以在一定程度上描述蛋白质表面残基侧链的柔性 ;双重过滤技术使更多的近天然结构保留下来 ,从而提高了算法成功预测的可能性 ;打分函数可以较合理地评价对接结构。总之 ,此种软对接算法能够对蛋白质分子识别的研究提供有益的帮助。     

蛋白质结构预测研究进展

蛋白质结构预测是生物信息学当前的主要挑战之一.按照蛋白质结构预测对PDB数据 库信息的依赖程度,可以将其划分成两类:模板依赖模型和从头预测方法.其中模板依赖模 型又可以分为同源模型与穿线法.本文介绍了各种预测方法主要步骤,分析了制约各种方法 的瓶颈,及其研究进展.同源模型所取得的结构精度较高,但其对模板依赖性强;用于低同 源性的穿线法是模板依赖的模型重要的研究方向;从头预测法中统计学函数与物理函数的综 合使用取得了很好的效果,但是对于超过150个残基的片段,依然是巨大的挑战.     

二级结构形成：蛋白质折叠起始过程的框架模型

框架模型认为二级结构形成是蛋白质起始过程的结构基础.文章介绍蛋白质同源片段的溶液构象及其构象研究法和多肽二级结构的从头设计,并综述这些研究成果应用于折叠起始过程的理论模型和蛋白质折叠起始过程的最新研究进展.     

蛋白质结构预测方法探析

首先介绍了蛋白质结构预测中的三种理论方法,然后对同源蛋白质结构预测中侧链构造和环区构建中涉及到的主要方法进行了探讨,对非同源蛋白质结构预测中空间构象搜寻涉及到的主要算法进行了分析比较。     

基于HP模型的蛋白质折叠问题的研究

基于蛋白质二维HP模型提出改进的遗传算法对真实蛋白质进行计算机折叠模拟。结果显示疏水能量函数最小值的蛋白质构象对应含疏水核心的稳定结构,疏水作用在蛋白质折叠中起主要作用。研究表明二维HP模型在蛋白质折叠研究中是可行的和有效的并为进一步揭示蛋白质折叠机理提供重要参考信息。     

基于遗传算法预测2D三向的蛋白质结构

本文基于范德华力势能预测2D三向的蛋白质结构。首先,将蛋白质结构预测这一生物问题转化为数学问题,并建立基于范德华力势能函数的数学模型。其次,使用遗传算法对数学模型进行求解,为了提高蛋白质结构预测效率,我们在标准遗传算法的基础上引入了调整算子这一概念,改进了遗传算法。最后,进行数值模拟实验。实验的结果表明范德华力势能函数模型是可行的,同时,和规范遗传算法相比,改进后的遗传算法能够较大幅度提高算法的搜索效率,并且遗传算法在蛋白质结构预测问题上有巨大潜力。     

一种基于SVR的分辨近天然G蛋白耦联受体—配体构象的方法

蛋白质小分子对接的难点之一是从生成的大量候选结构中挑选出近天然构象。本文使用了一种基于SVR的方法来挑选RosettaLigand生成的GPCR—配体decoy构象中的近天然构象。首先,对已有数据训练得到一个SVR模型,预测decoy构象的LRMSD,然后依此挑选近天然构象。最终,比较了本文方法和RosettaLigand方法挑选出的近天然构象decoy的质量,结果优于RosettaLigand方法,结果表明了本文方法能够有效地挑选出近天然构象。     

Ab initio protein structure prediction

Steady progress has been made in the field of ab initio protein folding. A variety of methods now allow the prediction of low-resolution structures of small proteins or protein fragments up to approximately 100 amino acid residues in length. Such low-resolution structures may be sufficient for the functional annotation of protein sequences on a genome-wide scale. Although no consistently reliable algorithm is currently available, the essential challenges to developing a general theory or approach to protein structure prediction are better understood. The energy landscapes resulting from the structure prediction algorithms are only partially funneled to the native state of the protein. This review focuses on two areas of recent advances in ab initio structure prediction-improvements in the energy functions and strategies to search the caldera region of the energy landscapes.     

Membrane protein prediction methods

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.     

Enhanced sampling near the native conformation using statistical potentials for local side-chain and backbone interactions

In the preceding article in this issue of Proteins, an empirical energy function consisting of 4 statistical potentials that quantify local side-chain-backbone and side-chain-side-chain interactions has been demonstrated to successfully identify the native conformations of short sequence fragments and the native structure within large sets of high-quality decoys. Because this energy function consists entirely of interactions between residues separated by fewer than 5 positions, it can be used at the earliest stage of ab initio structure prediction to enhance the efficiency of conformational search. In this article, protein fragments are generated de novo by recombining very short segments of protein structures (2, 4, or 6 residues), either selected at random or optimized with respect this local energy function. When local energy is optimized in selected fragments, more efficient sampling of conformational space near the native conformation is consistently observed for 450 randomly selected single turn fragments, with turn lengths varying from 3 to 12 residues and all 4 combinations of flanking secondary structure. These results further demonstrate the energetic significance of local interactions in protein conformations. When used in combination with longer range energy functions, application of these potentials should lead to more accurate prediction of protein structure.     

Application of statistical potentials to protein structure refinement from low resolution ab initio models

Recently ab initio protein structure prediction methods have advanced sufficiently so that they often assemble the correct low resolution structure of the protein. To enhance the speed of conformational search, many ab initio prediction programs adopt a reduced protein representation. However, for drug design purposes, better quality structures are probably needed. To achieve this refinement, it is natural to use a more detailed heavy atom representation. Here, as opposed to costly implicit or explicit solvent molecular dynamics simulations, knowledge-based heavy atom pair potentials were employed. By way of illustration, we tried to improve the quality of the predicted structures obtained from the ab initio prediction program TOUCHSTONE by three methods: local constraint refinement, reduced predicted tertiary contact refinement, and statistical pair potential guided molecular dynamics. Sixty-seven predicted structures from 30 small proteins (less than 150 residues in length) representing different structural classes (alpha, beta, alpha;/beta) were examined. In 33 cases, the root mean square deviation (RMSD) from native structures improved by more than 0.3 A; in 19 cases, the improvement was more than 0.5 A, and sometimes as large as 1 A. In only seven (four) cases did the refinement procedure increase the RMSD by more than 0.3 (0.5) A. For the remaining structures, the refinement procedures changed the structures by less than 0.3 A. While modest, the performance of the current refinement methods is better than the published refinement results obtained using standard molecular dynamics.     

De novo protein structure prediction by dynamic fragment assembly and conformational space annealing

Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods.     

Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field

Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field.     

Crystallographic molecular replacement using an in silico‐generated search model of SARS‐CoV‐2 ORF8

The majority of crystal structures are determined by the method of molecular replacement (MR). The range of application of MR is limited mainly by the need for an accurate search model. In most cases, pre‐existing experimentally determined structures are used as search models. In favorable cases, ab initio predicted structures have yielded search models adequate for MR. The ORF8 protein of SARS‐CoV‐2 represents a challenging case for MR using an ab initio prediction because ORF8 has an all β‐sheet fold and few orthologs. We previously determined experimentally the structure of ORF8 using the single anomalous dispersion (SAD) phasing method, having been unable to find an MR solution to the crystallographic phase problem. Following a report of an accurate prediction of the ORF8 structure, we assessed whether the predicted model would have succeeded as an MR search model. A phase problem solution was found, and the resulting structure was refined, yielding structural parameters equivalent to the original experimental solution.     

A graph-theory algorithm for rapid protein side-chain prediction

Fast and accurate side-chain conformation prediction is important for homology modeling, ab initio protein structure prediction, and protein design applications. Many methods have been presented, although only a few computer programs are publicly available. The SCWRL program is one such method and is widely used because of its speed, accuracy, and ease of use. A new algorithm for SCWRL is presented that uses results from graph theory to solve the combinatorial problem encountered in the side-chain prediction problem. In this method, side chains are represented as vertices in an undirected graph. Any two residues that have rotamers with nonzero interaction energies are considered to have an edge in the graph. The resulting graph can be partitioned into connected subgraphs with no edges between them. These subgraphs can in turn be broken into biconnected components, which are graphs that cannot be disconnected by removal of a single vertex. The combinatorial problem is reduced to finding the minimum energy of these small biconnected components and combining the results to identify the global minimum energy conformation. This algorithm is able to complete predictions on a set of 180 proteins with 34342 side chains in <7 min of computer time. The total chi(1) and chi(1 + 2) dihedral angle accuracies are 82.6% and 73.7% using a simple energy function based on the backbone-dependent rotamer library and a linear repulsive steric energy. The new algorithm will allow for use of SCWRL in more demanding applications such as sequence design and ab initio structure prediction, as well addition of a more complex energy function and conformational flexibility, leading to increased accuracy.     

Ab initio prediction of the three-dimensional structure of a de novo designed protein: a double-blind case study

Ab initio structure prediction and de novo protein design are two problems at the forefront of research in the fields of structural biology and chemistry. The goal of ab initio structure prediction of proteins is to correctly characterize the 3D structure of a protein using only the amino acid sequence as input. De novo protein design involves the production of novel protein sequences that adopt a desired fold. In this work, the results of a double-blind study are presented in which a new ab initio method was successfully used to predict the 3D structure of a protein designed through an experimental approach using binary patterned combinatorial libraries of de novo sequences. The predicted structure, which was produced before the experimental structure was known and without consideration of the design goals, and the final NMR analysis both characterize this protein as a 4-helix bundle. The similarity of these structures is evidenced by both small RMSD values between the coordinates of the two structures and a detailed analysis of the helical packing.