Motivation: Amyloidogenic regions in polypeptide chains are very important because such regions are responsible for amyloid formation and aggregation. It is useful to be able to predict positions of amyloidogenic regions in protein chains. Results: Two characteristics (expected probability of hydrogen bonds formation and expected packing density of residues) have been introduced by us to detect amyloidogenic regions in a protein sequence. We demonstrate that regions with high expected probability of the formation of backbone–backbone hydrogen bonds as well as regions with high expected packing density are mostly responsible for the formation of amyloid fibrils. Our method (FoldAmyloid) has been tested on a dataset of 407 peptides (144 amyloidogenic and 263 non-amyloidogenic peptides) and has shown good performance in predicting a peptide status: amyloidogenic or non-amyloidogenic. The prediction based on the expected packing density classified correctly 75% of amyloidogenic peptides and 74% of non-amyloidogenic ones. Two variants (averaging by donors and by acceptors) of prediction based on the probability of formation of backbone–backbone hydrogen bonds gave a comparable efficiency. With a hybrid-scale constructed by merging the above three scales, our method is correct for 80% of amyloidogenic peptides and for 72% of non-amyloidogenic ones. Prediction of amyloidogenic regions in proteins where positions of amyloidogenic regions are known from experimental data has also been done. In the proteins, our method correctly finds 10 out of 11 amyloidogenic regions. Availability: The FoldAmyloid server is available at http://antares.protres.ru/fold-amyloid/ Contact:ogalzit@vega.protres.ru

Guided by the recent success of empirical model predicting the folding rates of small two‐state folding proteins from the relative contact order (CO) of their native structures, by a theoretical model of protein folding that predicts that logarithm of the folding rate decreases with the protein chain length L as L^2/3, and by the finding that the folding rates of multistate folding proteins strongly correlate with their sizes and have very bad correlation with CO, we reexamined the dependence of folding rate on CO and L in attempt to find a structural parameter that determines folding rates for the totality of proteins. We show that the Abs_CO = CO × L, is able to predict rather accurately folding rates for both two‐state and multistate folding proteins, as well as short peptides, and that this Abs_CO scales with the protein chain length as L^{0.70 ± 0.07} for the totality of studied single‐domain proteins and peptides.

The determination of factors that influence protein conformational changes is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for eight of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of amyloid fibril formation is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value with which one can predict both intrinsically disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding. Protein folding is one of the most challenging issues in biophysical science. During the past few years it has been shown that some diseases are connected with protein misfolding and the formation of insoluble aggregates called amyloid plaques. These processes may be associated with several diseases such as Alzheimer disease, Parkinson disease, Creutzfeldt-Jacob disease, and even certain forms of cancer. It has been shown that proteins with intrinsically disordered regions are involved in protein–protein or protein–nucleic acid interactions. The main objective of this paper is to report insights into the molecular mechanisms of amyloid aggregation. This has been done using the parameter of the observed number of contacts for each amino acid residue in globular state, further called expected packing density. By analysis of sequences alone, the authors have demonstrated that regions that possess strong expected packing density can be responsible for amyloidogenic properties of a protein, while regions with weak expected packing density correspond to disordered regions. A new concept is proposed that could aid in understanding protein folding, misfolding, and amyloidosis. The results help to explain that the nature of the amyloidogenic propensity of proteins is connected to their amino acid sequences that are able to form a large number of contacts.

Summary: Identification of disordered regions in polypeptide chains is very important because such regions are essential for protein function. A new parameter, namely mean packing density of residues has been introduced to detect disordered regions in a protein sequence. We have demonstrated that regions with weak expected packing density would be responsible for the appearance of disordered regions. Our method (FoldUnfold) has been tested on datasets of globular proteins (559 proteins) and long disordered protein segments (129 proteins) and showed improved performance over some other widely used methods, such as DISOPRED, PONDR VL3H, IUPred and GlobPlot. Availability: The FoldUnfold server is available for users at . There is a link to our server through the web site of DisProt ( ). Contact:ogalzit@vega.protres.ru

We perform a large-scale study of intrinsically disordered regions in proteins and protein complexes using a non-redundant set of hundreds of different protein complexes. In accordance with the conventional view that folding and binding are coupled, in many of our cases the disorder-to-order transition occurs upon complex formation and can be localized to binding interfaces. Moreover, analysis of disorder in protein complexes depicts a significant fraction of intrinsically disordered regions, with up to one third of all residues being disordered. We find that the disorder in homodimers, especially in symmetrical homodimers, is significantly higher than in heterodimers and offer an explanation for this interesting phenomenon. We argue that the mechanisms of regulation of binding specificity through disordered regions in complexes can be as common as for unbound monomeric proteins. The fascinating diversity of roles of disordered regions in various biological processes and protein oligomeric forms shown in our study may be a subject of future endeavors in this area. Traditionally, protein structure is believed to determine function. Recently, it was observed that many proteins contain regions without well-defined structure (intrinsically disordered regions), including a large fraction of eukaryotic proteins. Intrinsic disorder has been associated with particular functions including cell regulation; signaling; and protein, DNA, and ligand binding. Many proteins are intrinsically disordered in native form and fold upon binding, following the conventional paradigm. Accordingly, disorder in a protein may facilitate binding to multiple partners. However, in some cases disorder has also been found in the bound state. To gain clearer insight into the functional importance of disorder regions in protein complexes, we perform a large-scale analysis of disorder using protein structures in complex and in unbound forms. We show that disorder in protein complexes is rather common and pinpoint changes that occur upon protein binding at interaction interfaces. By illustrating a variety of functional roles for disorder in specific proteins, we emphasize the versatility and importance of this phenomenon.