蛋白質複合體其次單元間演化偶合之研究

Abstract

在過去的研究已顯示蛋白質序列上氨基酸保留性走勢圖(sequence conservation profile)與其加權原子接觸數目走勢圖(weighted contact number profile)有關。此研究結果顯示著單一蛋白質結構蘊含其演化訊息。不過，在對於如何計算蛋白質復合物中一單體之接觸原子數目有著兩種不同方式–其一為考慮蛋白質復合物中其餘單體們的接觸數目之貢獻，另一為不考慮其餘單體之貢獻，兩方式差別在於是否考慮其餘單體們之貢獻。此篇研究中，我們選定酵素復合物之兩組實驗組: 組A與組B。組A中酵素之活性位氨基酸組成來自於不同單體氨基酸所提供，相反的，組B之活性位氨基酸組成則僅來自於單一單體。研究結果中，在組Ａ的酵素中，需考慮其餘單體們的接觸數目之貢獻所計算出的單體的接觸數目走勢圖相較於不考慮其餘單體之貢獻還來的與其序列保留性走勢圖更為接近，組B結果則相反。此結果可能在於組A相較於組B酵素有更強的功能與結構上的限制而去保持其行使功能的正常運作。此利用不同方式計算之加權原子接觸數目走勢圖與序列保留性走勢圖之比較或許提供了一個簡單的方式去了解蛋白質結構上與單體間演化偶和之訊息。 事實上，我們過去研究只專注於酵素之研究，很少應用在相較於酵素更複雜的系統，例如: 病毒蛋白質。接續的研究中，我們建立了包含51的不同源的20面病毒殼體(icosahedral capsids of viruses)。結果顯示，病毒間之單體有著很強的演化偶和來形成20面體，都需考慮其餘單體們的接觸數目之貢獻所計算出的單體的接觸數目走勢圖相較於不考慮其餘單體之貢獻還來的與其序列保留性走勢圖更為接近。 另一方面，結構生物學家長時間在研究病毒經常利用一種名為半徑表面塗色模型(radius-colored surface model)去描繪病毒之外形結構。此方法利用不同氨基酸到病毒結構中心點之距離的不同給與不同顏色。在這方法顏色顯示下，病毒表面上的圖案其類似的顏色中常被容易的標示為可能為遺傳物質進出所在或參與蛋白質結合所在。可是，科學家在利用半徑表面塗色模型就像個圖像顯示工具，本身並不帶有任何生物意義。此病毒研究中，我們發現了序列中氨基酸保留性可從其到病毒中心點距離所反映出來，也與其接觸數目有關。例如: 在病毒中越靠外層的氨基酸有著越大的半徑、越不保留、越小的接觸數目。同時，我們也發現由半徑表面塗色模型所形成之表面圖案與由序列保留性畫其表面圖案是雷同的。定量的連接序列保留性與結構上的關係也足以先顯示蛋白質序列保留性可以單從其結構推解出來。 從過去的研究指出，利用比較序列保留性走勢圖與其加權原子接觸數目走勢圖的方法，可以粗略的區分出單體間的演化偶和與屬於強還是弱的。事實上，我們嘗試著給一數值名為演化約束 (evolutionary constraint(α)) 把蛋白質單體間偶和的程度量化從0到1。 此研究中，我們選用過去使用的兩實驗組組A與組B。結果顯示量化結果可以把組A與組B進行區分。此外，我們比較了α與以往用來量化蛋白質單體間共同演化(coevolution)的方法。結果顯示兩方法間有著顯著與的正關係(P << 10-2, ρ = 0.64)。比較序列保留性走勢圖與其加權原子接觸數目走勢圖的方法或許提供給我們一個簡單可以量化蛋白質單體間偶合的方法。

We have recently showed that the weighted contact number profiles, packing profiles, of proteins are well correlated with those of the corresponding sequence conservation profiles. The results suggest that a protein structure may contain sufficient information about sequence conservation comparable to that derived from multiple homologous sequences. However, there are ambiguities concerning how to compute the packing density of the subunit of a protein complex. For the subunits of a complex, there are different ways to compute its packing density – one including the packing contributions of the other subunits and the other one excluding their contributions. Here we selected two sets, Set A and Set B, of enzyme complexes. Set A contains complexes with the active sites comprising residues from multiple subunits, while set B contains those with the active sites residing on single subunits. In Set A, if the packing density profile of a subunit is computed considering the contributions of the other subunits of the complex, it will agree better with the sequence conservation profile. But in Set B the situations are reversed. The results may be due to the stronger functional and structural constraints on the evolution processes on the complexes of Set A than those of Set B to maintain the enzymatic functions of the complexes. The results show the sequence conservation profiles agree with the packing profile of including other subunits contributions. The comparison of the packing density and the sequence conservation profiles may provide a simple yet potentially useful way to understanding the structural and evolutionary couplings between the subunits of protein complexes. Actually, the previous studies have been done on enzymes; little information is available on other more complicated proteins than enzymes such as viral proteins. We built a dataset of 51 crystal structures of nonhomologous icosahedral capsids of viruses, which is more complex dataset than the dataset of enzyme complexes, Set A and Set B. We found that for all icosahedral capsids of the dataset, which have strong evolutionary couplings between their subunits, the sequence conservation profile of its subunit will agree better with the packing profile, which includes the contributions of other subunits. On the other hand, structural biologists have long used the so-called radius-colored surface model in 3D graphics to render the structures of icosahedral capsids of viruses. In this model, the residues on the virus surface are color-coded radially from the centroid of the capsid. In this way, interesting surface patterns emerge on capsids. These surface patterns, which have similar color, are often indicative of biological functionality, such as the surface pore associated with the export/import of genomic material or molecular components or surface clefts involved in specific binding. But, the radial-colored surface model is often considered merely as an image enhancement tool without any biological bases. Here, we reported that, of the viral icosahedral capsids, the sequence conservation profile could be determined by variations in the distances between residues and the centroid of the capsid – with a direct inverse proportionality between the conservation level and the centroid distance – as well as by the spatial variations in local packing density. Examining both the centroid and the packing density models against a dataset of 51 crystal structures of nonhomologous icosahedral capsids, we find that they consistently reproduce the global patterns and many minor features in the sequence conservation profile with sufficient accuracy. The quantitative link between the level of conservation and structural features like centroid-distance or packing density found here indicates that the levels of conservation of residues can be determined by their atomic coordinates. Finally, we have indicated that using the approach, comparing the two packing profiles of the subunit of a protein complex – including and excluding other subunits contributions – with the sequence conservation profile, would roughly identify a protein complex with a strong or weak coupling. Actually, we found that the evolutionary coupling between subunits could be quantified instead of being roughly identified. Here we attempted to assign an α score, evolutionary constraint (α), from 0 to 1, to quantify the evolutionary coupling between the subunits of a protein. We used 2 sets of enzyme complexes: Set A contained complexes of which the active sites comprised residues from multiple subunits, and Set B contained complexes of which the active sites were located on single subunits. In the results, all of the protein complexes were separated into 2 clusters, as the datasets of Sets A and B according to the α score. Additionally, we used current coevolution methods at the protein level to estimate the degree of couplings between the subunits of a protein. Comparing the scores between coevolution and the proposed method revealed significant (P << 10-2) and positive correlations (ρ = 0.64). The relationship between the packing density and the sequence conservation profiles provide a simple approach to quantifying evolutionary coupling between subunits.