Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3-dimensional protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold2, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death, and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to the modeling of the complex structures by AlphaFold2, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most of the top ranked pairs with high contact probability were supported by known protein-protein interactions and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2, ECSIT-NDUFAF1 and COQ7-COQ9, among others. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in.

Slow chromatin dynamics enhances promoter accessibility to transcriptional condensates

Enhancers are DNA sequences at a long genomic distance from target genes. Recent experiments suggest that enhancers are anchored to the surfaces of condensates of transcription machinery and that the loop extrusion process enhances the transcription level of their target genes. Here we theoretically study the polymer dynamics driven by the loop extrusion of the linker DNA between an enhancer and the promoter of its target gene to calculate the contact probability of the promoter to the transcription machinery in the condensate. Our theory predicts that when the loop extrusion process is active, the contact probability increases with increasing linker DNA length. This finding reflects the fact that the relaxation time, with which the promoter stays in proximity to the surface of the transcriptional condensate, increases as the length of the linker DNA increases. This contrasts the equilibrium case for which the contact probability between the promoter and the transcription machineries is smaller for longer linker DNA lengths.

Lipid contact probability: an essential and predictive character for the structural and functional studies of membrane proteins

One of the unique traits of membrane proteins is that a significant fraction of their hydrophobic amino acids is exposed to the hydrophobic core of lipid bilayers rather than being embedded in the protein interior, which is often not explicitly considered in protein structure prediction. Here, we propose a characteristic and predictive quantity, the lipid contact probability (LCP), to describe the likelihood of the amino acids of a given sequence being in direct contact with the acyl chains of lipid molecules. We show that LCP is complementary to solvent accessibility in characterizing the outer surface of membrane proteins, and it can be predicted for any given sequence with a machine learning-based method by utilizing a training dataset extracted from MemProtMD, a database generated from molecular dynamics simulations for the membrane proteins with a known structure. We demonstrate that LCP can be used to systematically improve the prediction accuracy of the contact maps of proteins, as well as to identify membrane-anchored soluble proteins from sequences.

The role of alpha-helix on the structure-targeting drug design of amyloidogenic proteins

The most accredited hypothesis links the toxicity of amyloid proteins to their harmful effects on membrane integrity through the formation of prefibrillar-transient oligomers able to disrupt cell membranes. However, damage mechanisms necessarily assume a first step in which the amyloidogenic protein transfers from the aqueous phase to the membrane hydrophobic core. This determinant step is still poorly understood. However, according to our lipid-chaperon hypothesis, free lipids in solution play a crucial role in facilitating this footfall. Free phospholipid concentration in the aqueous phase acts as a switch between ion channel-like pore and fibril formation, so that high free lipid concentration in solution promotes pore and repress fibril formation. Conversely, low free lipids in the solution favor fibril and repress pore formation. This behavior is due to the formation of stable lipid-protein complexes. Here, we hypothesize that the helix propensity is a fundamental requirement to fulfill the lipid-chaperon model. The alpha-helix region seems to be responsible for the binding with amphiphilic molecules fostering the proposed mechanism. Indeed, our results show the dependency of protein-lipid binding from the helical structure presence. When the helix content is substantially lower than the wild type, the contact probability decreases. Instead, if the helix is broadening, the contact probability increases. Our findings open a new perspective for in silico screening of secondary structure-targeting drugs of amyloidogenic proteins.