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The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

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Malaria disease is caused by the transmission of Plasmodium, through the bite of a female Anopheles mosquito. Although the Plasmodium life-cycle has been extensively characterized, relatively little is known about sporozoite interaction with host organelles and proteins. Individuals that survive continuous exposure to infection do eventually develop clinical immunity, suggesting that a vaccine against asexual blood stage of the parasite is achievable. The merozoite surface protein (MSP1 19 ) of Plasmodium yoelii was considered as the target protein for epitope prediction using the computational approaches. The T-cell and B-cell epitopes for MSP1 19 were predicted using a variety of computational tools. Out of these predicted epitopes, the epitopes being expressed by the protozoa were identified. The 3D structures of T-cell epitopes (MHC-I and MHC-II) were modeled by homology modeling method followed by validation using the SAVES server. Further, the MHC molecules were identified and their 3D structures were retrieved from the Protein Data Bank. The protein-protein docking of modeled epitopes with respective MHC molecules were also carried out. Total Six T-cell epitopes ('ELSEHYYDRY', 'LLIITIVFNI', 'MMYHIYKLK', 'IYQAMYNVIF', 'SEEDMPADDF', 'YVLLQNSTI') for MSP1 19 have been identified as promising vaccine candidates. Furthermore, six B-cell epitopes ('QPTET', 'SEETE', 'SDKYNKKKP', 'KEKKKE', 'CKKNKA', 'THPDNT') have also been identified as potential epitopes. In future, these predicted epitopes might be exploited in vaccine development against malarial infection.